Training of speech recognition systems

ABSTRACT

A method may include obtaining first audio data of a first communication session between a first and second device and during the first communication session, obtaining a first text string that is a transcription of the first audio data and training a model of an automatic speech recognition system using the first text string and the first audio data. The method may further include in response to completion of the training, deleting the first audio data and the first text string and after deleting the first audio data and the first text string, obtaining second audio data of a second communication session between a third and fourth device and during the second communication session obtaining a second text string that is a transcription of the second audio data and further training the model of the automatic speech recognition system using the second text string and the second audio data.

FIELD

The embodiments discussed herein are related to transcriptions ofcommunications.

BACKGROUND

Transcriptions of audio communications between people may assist peoplethat are hard-of-hearing or deaf to participate in the audiocommunications. Transcription of audio communications may be generatedwith assistance of humans or may be generated without human assistanceusing automatic speech recognition (“ASR”) systems. After generation,the transcriptions may be provided to a device for display.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

SUMMARY

In some embodiments, a method may include obtaining first audio data ofa first communication session between a first device of a first user anda second device of a second user. In these and other embodiments, thefirst communication session may be configured for verbal communication.The method may also include obtaining, during the first communicationsession, a first text string that is a transcription of the first audiodata and training, during the first communication session, a model of anautomatic speech recognition system using the first text string and thefirst audio data. The method may further include in response tocompletion of the training of the model using the first text string andthe first audio data, deleting the first audio data and the first textstring and after deleting the first audio data and the first textstring, obtaining second audio data of a second communication sessionbetween a third device of a third user and a fourth device of a fourthuser. The method may further include obtaining, during the secondcommunication session, a second text string that is a transcription ofthe second audio data and further training, during the secondcommunication session, the model of the automatic speech recognitionsystem using the second text string and the second audio data.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example environment for transcription ofcommunications;

FIG. 2 illustrates another example environment for transcription ofcommunications;

FIG. 3 is a flowchart of an example method to select a transcriptionunit;

FIG. 4 illustrates another example environment for transcription ofcommunications;

FIG. 5 is a schematic block diagram illustrating an environment forspeech recognition;

FIG. 6 is a flowchart of an example method to transcribe audio;

FIG. 7 is a flowchart of another example method to transcribe audio;

FIG. 8 is a flowchart of another example method to transcribe audio;

FIG. 9 is a schematic block diagram illustrating an exampletranscription unit;

FIG. 10 is a schematic block diagram illustrating another exampletranscription unit;

FIG. 11 is a schematic block diagram illustrating another exampletranscription unit;

FIG. 12 is a schematic block diagram illustrating multiple transcriptionunits;

FIG. 13 is a schematic block diagram illustrating combining the outputof multiple automatic speech recognition (ASR) systems;

FIG. 14 illustrates a data flow to fuse multiple transcriptions;

FIG. 15 illustrates an example environment for adding capitalization andpunctuation to a transcription;

FIG. 16 illustrates an example environment for providing capitalizationand punctuation to fused transcriptions;

FIG. 17 illustrates an example environment for transcription ofcommunications;

FIG. 18 illustrates another example environment for transcription ofcommunications;

FIG. 19 illustrates another example environment for transcription ofcommunications;

FIG. 20 illustrates another example environment for transcription ofcommunications;

FIG. 21 illustrates another example environment for selecting betweentranscriptions;

FIG. 22 is a schematic block diagram depicting an example embodiment ofa scorer;

FIG. 23 is a schematic block diagram depicting another exampleembodiment of a scorer;

FIG. 24 is a schematic block diagram illustrating an example embodimentof a selector;

FIG. 25 is a schematic block diagram illustrating an example embodimentof a selector;

FIG. 26 is a schematic block diagram illustrating another exampleembodiment of a selector;

FIGS. 27a and 27b illustrate embodiments of a linear estimator and anon-linear estimator respectively;

FIG. 28 is a flowchart of an example method of selecting betweentranscription units;

FIG. 29 is a flowchart of another example method of selecting betweentranscription units;

FIG. 30 is a flowchart of another example method of selecting betweentranscription units;

FIG. 31 illustrates another example environment for transcription ofcommunications;

FIGS. 32a and 32b illustrate example embodiments of transcription units;

FIGS. 33a, 33b, and 33c are schematic block diagrams illustratingexample embodiments of transcription units;

FIG. 34 is another example embodiment of a transcription unit;

FIG. 35 is a schematic block diagram illustrating an example environmentfor editing by a captioning assistant (CA);

FIG. 36 is a schematic block diagram illustrating an example environmentfor sharing audio among CA clients;

FIG. 37 is a schematic block diagram illustrating an exampletranscription unit;

FIG. 38 illustrates another example transcription unit;

FIG. 39 illustrates an example environment for transcription generation;

FIG. 40 illustrates an example environment that includes a multipleinput ASR system;

FIG. 41 illustrates an example environment for determining an audiodelay;

FIG. 42 illustrates an example environment where a first ASR systemguides the results of a second ASR system;

FIG. 43 is a flowchart of another example method of fusingtranscriptions;

FIG. 44 illustrates an example environment for scoring a transcriptionunit;

FIG. 45 illustrates another example environment for scoring atranscription unit;

FIG. 46 illustrates an example environment for generating an estimatedaccuracy of a transcription;

FIG. 47 illustrates another example environment for generating anestimated accuracy of a transcription;

FIG. 48 illustrates an example audio delay;

FIG. 49 illustrates an example environment for measuring accuracy of atranscription service;

FIG. 50 illustrates an example environment for measuring accuracy;

FIG. 51 illustrates an example environment for testing accuracy oftranscription units;

FIG. 52 illustrates an example environment for equivalency maintenance;

FIG. 53 illustrates an example environment for denormalization machinelearning;

FIG. 54 illustrates an example environment for denormalizing text;

FIG. 55 illustrates an example fuser;

FIG. 56 illustrates an example environment for training an ASR system;

FIG. 57 illustrates an example environment for using data to trainmodels;

FIG. 58 illustrates an example environment for training models;

FIG. 59 illustrates an example environment for using trained models;

FIG. 60 illustrates an example environment for selecting data samples;

FIG. 61 illustrates an example environment for training language models;

FIG. 62 illustrates an example environment for training models in one ormore central locations;

FIG. 63 is a flowchart of an example method of collecting and usingn-grams to train a language model;

FIG. 64 is a flowchart of an example method of filtering n-grams forprivacy;

FIG. 65 illustrates an example environment for distributed collection ofn-grams;

FIG. 66 is a flowchart of an example method of n-gram training;

FIG. 67 illustrates an example environment for neural net language modeltraining;

FIG. 68 illustrates an example environment for distributed modeltraining;

FIG. 69 illustrates an example environment for a centralized speechrecognition and model training;

FIG. 70 illustrates an example environment for training models fromfused transcriptions;

FIG. 71 illustrates an example environment for training models ontranscriptions from multiple processing centers;

FIG. 72 illustrates an example environment for distributed modeltraining;

FIG. 73 illustrates an example environment for distributed modeltraining;

FIG. 74 illustrates an example environment for distributed modeltraining;

FIG. 75 illustrates an example environment for subdividing modeltraining;

FIG. 76 illustrates an example environment for subdividing modeltraining;

FIG. 77 illustrates an example environment for subdividing a model;

FIG. 78 illustrates an example environment for training modelson-the-fly;

FIG. 79 is a flowchart of an example method of on-the-fly modeltraining;

FIG. 80 illustrates an example system for speech recognition;

FIG. 81 illustrates an example environment for selecting between models;

FIG. 82 illustrate an example ASR system using multiple models;

FIG. 83 illustrates an example environment for adapting or combiningmodels; and

FIG. 84 illustrates an example computing system that may be configuredto perform operations and method disclosed herein, all arranged inaccordance with one or more embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Some embodiments in this disclosure relate to systems and methods thatmay be configured to transcribe audio of a communication session. Forexample, in some embodiments, audio of a communication session may beprovided to a transcription system to transcribe the audio from a devicethat receives and/or generates the audio. A transcription of the audiogenerated by the transcription system may be provided back to the devicefor display to a user of the device. The transcription may assist theuser to better understand what is being said during the communicationsession.

For example, a user may be hard of hearing and participating in a phonecall. Because the user is hard of hearing, the user may not understandeverything being said during the phone call from the audio of the phone.However, the audio may be provided to a transcription system. Thetranscription system may generate a transcription of the audio inreal-time during the phone call and provide the transcription to adevice of the user. The device may present the transcription to theuser. Having a transcription of the audio may assist the hard of hearinguser to better understand the audio and thereby better participate inthe phone call.

Presenting transcriptions that are inaccurate or that lag behind theaudio of the communication session may reduce the benefit of thetranscription. Thus, the systems and methods described in someembodiments may be directed to reducing the inaccuracy of transcriptionsand a time required to generate transcriptions. Additionally, thesystems and methods described in some embodiments may be directed toreducing costs to generate transcriptions. Reduction of costs may maketranscriptions available to more people. In some embodiments, thesystems and methods described in this disclosure may reduce inaccuracy,time, and/or costs by incorporating a fully automatic speech recognition(ASR) system into a transcription system.

Some current systems may use ASR systems in combination with humanassistance to generate transcriptions. For example, some current systemsmay employ humans to revoice audio from a communication session. Therevoiced audio may be provided to an ASR system that may generate atranscription based on the revoiced audio. Revoicing may cause delays ingeneration of the transcription and may increase expenses. Additionally,the transcription generated based on the revoiced audio may includeerrors.

In some embodiments, systems and methods in this disclosure may beconfigured to select between different transcription systems and/ormethods. Alternatively or additionally, systems and methods in thisdisclosure may be configured to switch between different transcriptionsystems and/or methods during a communication session. The selection ofdifferent systems and/or methods and switching between different systemsand/or methods, may, in some embodiments, reduce costs, reducetranscription delays, or provide other benefits. For example, anautomatic system that uses automatic speech recognition may begintranscription of audio of a communication session. During thecommunication session, a revoicing system, which uses human assistanceas described above, may assume responsibility to generate transcriptionsfor a remainder of the communication session. Some embodiments of thisdisclosure discuss factors regarding how a particular system and/ormethod may be selected, why a switch between different systems and/ormethods may occur, and how the selection and switching may occur.

In some embodiments, systems and methods in this disclosure may beconfigured to combine or fuse multiple transcriptions into a singletranscription that is provided to a device for display to a user. Fusingmultiple transcriptions may assist a transcription system to produce amore accurate transcription with fewer errors. In some embodiments, themultiple transcriptions may be generated by different systems and/ormethods. For example, a transcription system may include an automaticASR system and a revoicing system. Each of the automatic ASR system andthe revoicing system may generate a transcription of audio of acommunication session. The transcriptions from each of the automatic ASRsystem and the revoicing system may be fused together to generate afinalized transcription that may be provided to a device for display.

In some embodiments, systems and methods in this disclosure may beconfigured to improve the accuracy of ASR systems used to transcribe theaudio of communication sessions. In these and other embodiments,improving the accuracy of an ASR system may include improving an abilityof the ASR system to recognize words in speech.

In some embodiments, the accuracy of an ASR system may be improved bytraining ASR systems using live audio. For example, the audio of a livecommunication session may be used to train an ASR system. Alternativelyor additionally, the accuracy of an ASR system may be improved byobtaining an indication of a frequency that a sequence of words, such asa sequence of two to four words, are used during speech. In these andother embodiments, sequences of words may be extracted fromtranscriptions of communication sessions. A count for each particularsequence of words may be incremented each time the particular sequenceof words is extracted. The counts for each particular sequence of wordsmay be used to improve the ASR systems.

Thus, the systems and methods described in this disclosure may result inthe improved display of transcriptions at a user device. Furthermore,the systems and methods described in this disclosure may improvetechnology with respect to audio transcriptions and real-time generationand display of audio transcriptions. Additionally, the systems andmethods described in this disclosure may improve technology with respectto automatic speech recognition.

Turning to the figures, FIG. 1 illustrates an example environment 100for transcription of communications. The environment 100 may be arrangedin accordance with at least one embodiment described in the presentdisclosure. The environment 100 may include a network 102, a firstdevice 104, a second device 106, and a transcription system 108 that mayinclude a transcription unit 114, each of which will be described ingreater detail below.

The network 102 may be configured to communicatively couple the firstdevice 104, the second device 106, and the transcription system 108. Insome embodiments, the network 102 may be any network or configuration ofnetworks configured to send and receive communications between systemsand devices. In some embodiments, the network 102 may include aconventional type network, a wired network, an optical network, and/or awireless network, and may have numerous different configurations. Insome embodiments, the network 102 may also be coupled to or may includeportions of a telecommunications network, including telephone lines, forsending data in a variety of different communication protocols, such asa plain old telephone system (POTS).

As an example, the network 102 may include a POTS network that maycouple the first device 104 and the second device 106, and awired/optical network and a wireless network that may couple the firstdevice 104 and the transcription system 108. In these and otherembodiments, the network 102 may not be a conjoined network. Forexample, the network 102 may represent separate networks and theelements in the environment 100 may route data between the separatenetworks. In short, the elements in the environment 100 may be coupledtogether such that data may be transferred there by the network 102using any known method or system.

Each of the first and second devices 104 and 106 may be any electronicor digital computing device. For example, each of the first and seconddevices 104 and 106 may include a desktop computer, a laptop computer, asmartphone, a mobile phone, a video phone, a tablet computer, atelephone, a speakerphone, a VoIP phone, a smart speaker, a phoneconsole, a caption device, a captioning telephone, a communicationsystem in a vehicle, a wearable device such as a watch or pair ofglasses configured for communication, or any other computing device thatmay be used for communication between users of the first and seconddevices 104 and 106.

In some embodiments, each of the first device 104 and the second device106 may include memory and at least one processor, which are configuredto perform operations as described in this disclosure, among otheroperations. In some embodiments, each of the first device 104 and thesecond device 106 may include computer-readable instructions that areconfigured to be executed by each of the first device 104 and the seconddevice 106 to perform operations described in this disclosure.

In some embodiments, each of the first and second devices 104 and 106may be configured to establish communication sessions with otherdevices. For example, each of the first and second devices 104 and 106may be configured to establish an outgoing communication session, suchas a telephone call, video call, or other communication session, withanother device over a telephone line or network. For example, each ofthe first device 104 and the second device 106 may communicate over aWiFi network, wireless cellular network, a wired Ethernet network, anoptical network, or a POTS line.

In some embodiments, each of the first and second devices 104 and 106may be configured to obtain audio during a communication session. Theaudio may be part of a video communication or an audio communication,such as a telephone call. As used in this disclosure, the term audio maybe used generically to refer to sounds that may include spoken words.Furthermore, the term “audio” may be used generically to include audioin any format, such as a digital format, an analog format, or apropagating wave format. Furthermore, in the digital format, the audiomay be compressed using different types of compression schemes. Also, asused in this disclosure, the term video may be used generically to referto a compilation of images that may be reproduced in a sequence toproduce video.

As an example of obtaining audio, the first device 104 may be configuredto obtain first audio from a first user 110. The first audio may includea first voice of the first user 110. The first voice of the first user110 may be words spoken by the first user. For example, the first device104 may obtain the first audio from a microphone of the first device 104or from another device that is communicatively coupled to the firstdevice 104.

The second device 106 may be configured to obtain second audio from asecond user 112. The second audio may include a second voice of thesecond user 112. The second voice of the second user 112 may be wordsspoken by the second user. In some embodiments, second device 106 mayobtain the second audio from a microphone of the second device 106 orfrom another device communicatively coupled to the second device 106.During a communication session, the first device 104 may provide thefirst audio to the second device 106. Alternatively or additionally, thesecond device 106 may provide the second audio to the first device 104.Thus, during a communication session, both the first device 104 and thesecond device 106 may obtain both the first audio from the first user110 and the second audio from the second user 112.

In some embodiments, one or both of the first device 104 and the seconddevice 106 may be configured to provide the first audio, the secondaudio, or both the first audio and the second audio to the transcriptionsystem 108. In these and other embodiments, in addition to providing thefirst audio, the second audio, or both the first audio and the secondaudio to the transcription system 108, one or both of the first device104 and the second device 106 may be configured to extract speechrecognition features from the first audio, the second audio, or both thefirst audio and the second audio. In some embodiments, after extractingthe speech recognition features, the features may be quantized orotherwise compressed. The extracted features may be provided to thetranscription system 108 via the network 102.

In some embodiments, the transcription system 108 may be configured togenerate a transcription of the audio received from either one or bothof the first device 104 and the second device 106. The transcriptionsystem 108 may also provide the generated transcription of the audio toeither one or both of the first device 104 and the second device 106.Either one or both of the first device 104 and the second device 106 maybe configured to present the transcription received from thetranscription system 108. For example, audio of both the first user 110and the second user 112 may be provided to the transcription system 108.In these and other embodiments, transcription of the first audio may beprovided to the second device 106 for the second user 112 andtranscription of the second audio may be provided to the first device104 for the first user 110. In some embodiments, the disclosure may alsoindicate that a person is receiving the transcriptions from thetranscription system 108. In these and other embodiments, a deviceassociated with the person may receive the transcriptions from thetranscription system 108 and the transcriptions may be presented to theperson by the device. In this manner, a person may receive thetranscription.

The transcription system 108 may include any configuration of hardware,such as processors, servers, and storage servers, such as databaseservers, that are networked together and configured to perform one ormore task. For example, the transcription system 108 may include one ormultiple computing systems, such as multiple servers that each includememory and at least one processor.

In general, the transcription system 108 may be configured to obtainaudio from a device, generate or direct generation of a transcription ofthe audio, and provide the transcription of the audio to the device oranother device for presentation of the transcription. This disclosuredescribes various configurations of the transcription system 108 andvarious methods performed by the transcription system 108 to generate ordirect generation of transcriptions of audio.

In general, the transcription system 108 may be configured to generateor direct generation of the transcription of audio using one or moreautomatic speech recognition (ASR) systems. The term “ASR system” asused in this disclosure may include a compilation of hardware, software,and/or data, such as trained models, that are configured to recognizespeech in audio and generate a transcription of the audio based on therecognized speech. For example, in some embodiments, an ASR system maybe a compilation of software and data models. In these and otherembodiments, multiple ASR systems may be included on a computer system,such as a server. Alternatively or additionally, an ASR system may be acompilation of hardware, software, and data models. In these and otherembodiments, the ASR system may include the computer system. In someembodiments, the transcription of the audio generated by the ASR systemsmay include capitalization, punctuation, and non-speech sounds. Thenon-speech sounds may include, background noise, vocalizations such aslaughter, filler words such as “um,” and speaker identifiers such as“new speaker,” among others.

The ASR systems used by the transcription system 108 may be configuredto operate in one or more locations. The locations may include thetranscription system 108, the first device 104, the second device 106,another electronic computing device, or at an ASR service that iscoupled to the transcription system 108 by way of the network 102. TheASR service may include a service that provides transcriptions of audio.Example ASR services include services provided by Google®, Microsoft®,and IBM®, among others.

In some embodiments, the ASR systems described in this disclosure may beseparated into one of two categories: speaker-dependent ASR systems andspeaker-independent ASR systems. In some embodiments, aspeaker-dependent ASR system may use a speaker-dependent speech model. Aspeaker-dependent speech model may be specific to a particular person ora group of people. For example, a speaker-dependent ASR systemconfigured to transcribe a communication session between the first user110 and the second user 112 may include a speaker-dependent speech modelthat may be specifically trained using speech patterns for either orboth the first user 110 and the second user 112.

In some embodiments, a speaker-independent ASR system may be trained ona speaker-independent speech model. A speaker-independent speech modelmay be trained for general speech and not specifically trained usingspeech patterns of the people for which the speech model is employed.For example, a speaker-independent ASR system configured to transcribe acommunication session between the first user 110 and the second user 112may include a speaker-independent speech model that may not bespecifically trained using speech patterns for the first user 110 or thesecond user 112. In these and other embodiments, the speaker-independentspeech model may be trained using speech patterns of users of thetranscription system 108 other than the first user 110 and the seconduser 112.

In some embodiments, the audio used by the ASR systems may be revoicedaudio. Revoiced audio may include audio that has been received by thetranscription system 108 and gone through a revoicing process. Therevoicing process may include the transcription system 108 obtainingaudio from either one or both of the first device 104 and the seconddevice 106. The audio may be broadcast by a captioning agent (CA) clientfor a captioning agent (CA) 118 associated with the transcription system108. The CA client may broadcast or direct broadcasting of the audiousing a speaker. The CA 118 listens to the broadcast audio and speaksthe words that are included in the broadcast audio. The CA client may beconfigured to capture or direct capturing of the speech of the CA 118.For example, the CA client may use or direct use of a microphone tocapture the speech of the CA 118 to generate revoiced audio.

The term “revoiced audio” as used in this disclosure may refer to audiogenerated as discussed above. In this disclosure, the use of the termaudio generally may refer to both audio that results from acommunication session between devices without revoicing and revoicedaudio. In embodiments where a distinction is being made between audiowithout revoicing and revoiced audio, the audio without revoicing may bereferred to as regular audio.

In some embodiments, revoiced audio may be provided to aspeaker-independent ASR system. In these and other embodiments, thespeaker-independent ASR system may not be specifically trained usingspeech patterns of the CA revoicing the audio. Alternatively oradditionally, revoiced audio may be provided to a speaker-dependent ASRsystem. In these and other embodiments, the speaker-dependent ASR systemmay be specifically trained using speech patterns of the CA revoicingthe audio.

In some embodiments, the transcription system 108 may include one ormore transcription units, such as the transcription unit 114. In someembodiments, a transcription unit as used in this disclosure may beconfigured to obtain audio and to generate a transcription of the audio.In some embodiments, a transcription unit may include one or more ASRsystems. In these and other embodiments, the one or more ASR systems maybe speaker-independent, speaker-dependent, or some combination ofspeaker-independent and speaker-dependent. Alternatively oradditionally, a transcription unit may include other systems that may beused in generating a transcription of audio. For example, the othersystems may include a fuser, a text editor, a model trainer, diarizer,denormalizer, comparer, counter, adder, accuracy estimator, among othersystems. Each of these systems is described later with respect to someembodiments in the present disclosure.

In some embodiments, a transcription unit may obtain revoiced audio fromregular audio to generate a transcription. In these and otherembodiments, when the transcription unit uses revoiced audio, thetranscription unit may be referred to in this disclosure as a revoicedtranscription unit. Alternatively or additionally, when thetranscription unit does not use revoiced audio, the transcription unitmay be referred to in this disclosure as a non-revoiced transcriptionunit. In some embodiments, a transcription unit may use a combination ofaudio and revoicing of the audio to generate a transcription. Forexample, a transcription unit may use regular audio, first revoicedaudio from the first CA, and second revoiced audio from a second CA.

An example transcription unit may include the transcription unit 114.The transcription unit 114 may include a first ASR system 120 a, asecond ASR system 120 b, and a third ASR system 120 c. In general, thefirst ASR system 120 a, the second ASR system 120 b, and the third ASRsystem 120 c may be referred to as ASR systems 120. The transcriptionunit 114 may further include a fuser 124 and a CA client 122.Alternatively or additionally, the transcription system 108 may includethe CA client 122 and the transcription unit 114 may interface with theCA client 122.

In some embodiments, the CA client 122 may be configured to obtainrevoiced audio from a CA 118. In these and other embodiments, the CAclient 122 may be associated with the CA 118. The CA client 122 beingassociated with the CA 118 may indicate that the CA client 122 presentstext and audio to the CA 118 and obtains input from the CA 118 through auser interface. In some embodiments, the CA client 122 may operate on adevice that includes input and output devices for interacting with theCA 118, such as a CA workstation. Alternatively or additionally, the CAclient 122 may be hosted on a server on a network and a device thatincludes input and output devices for interacting with the CA 118 may bea thin client networked with server that may be controlled by the CAclient 122.

In some embodiments, the device associated with the CA client 122 mayinclude any electronic device, such as a personal computer, laptop,tablet, mobile computing device, mobile phone, and a desktop, amongother types of devices. In some embodiments, the device may include thetranscription unit 114. For example, the device may include the hardwareand/or software of the ASR systems 120, the CA client 122, and/or thefuser 124. Alternatively or additionally, the device may be separatefrom the transcription unit 114. In these and other embodiments, thetranscription unit 114 may be hosted by a server that may also beconfigured to host the CA client 122. Alternatively or additionally, theCA client 122 may be part of the device and the remainder of thetranscription unit 114 may be hosted by one or more servers. Thus,various configurations of the transcription unit 114 are possible andare contemplated outside of the configurations discussed above.Furthermore, a discussion of a transcription unit in this disclosuredoes not imply a certain physical configuration of the transcriptionunit. Rather, a transcription unit as used in this disclosure provides asimplified way to describe interactions between different systems thatare configured to generate a transcription of audio. In short, atranscription unit as described may include any configuration of thesystems described in this disclosure to accomplish the transcription ofaudio. The systems used in a transcription unit may be located, hosted,or otherwise configured across multiple devices, such as servers andother devices, in a network. Furthermore, the systems from onetranscription unit may not be completely separated from systems fromanother transcription unit. Rather, systems may be shared acrossmultiple transcription units.

In some embodiments, the transcription system 108 may obtain audio fromthe communication session between the first device 104 and the seconddevice 106. In these and other embodiments, the transcription system 108may provide the audio to the transcription unit 114. The transcriptionunit 114 may be configured to provide the audio to the CA client 122

In some embodiments, the CA client 122 may be configured to receive theaudio from the transcription unit 114 and/or the transcription system108. The CA client 122 may broadcast the audio for the CA 118 through aspeaker. The CA 118 may listen to the audio and revoice or re-speak thewords in the broadcast audio. In response to broadcasting the audio, theCA client 122 may use a microphone to capture the speech of the CA 118.The CA client 122 may generate revoiced audio using the captured speechof the CA 118. In some embodiments, the CA client 122 may provide therevoiced audio to one or more of the ASR systems 120 in thetranscription unit 114.

In some embodiments, the first ASR system 120 a may be configured toobtain the revoiced audio from the CA client 122. In these and otherembodiments, the first ASR system 120 a may also be configured asspeaker-dependent with respect to the speech patterns of the CA 118. Thefirst ASR system 120 a may be speaker-dependent with respect to thespeech patterns of the CA 118 by using models trained using the speechpatterns of the CA 118. The models trained using the speech patterns ofthe CA 118 may be obtained from a CA profile of the CA 118. The CAprofile may be obtained from the CA client 122 and/or from a storagedevice associated with the transcription unit 114 and/or thetranscription system 108.

In these and other embodiments, the CA profile may include one or moreASR modules that may be trained with respect to the speaker profile ofthe CA 118. The speaker profile may include models or links to modelssuch as acoustic models and feature transformation models such as neuralnetworks or MLLR or fMLLR transforms. The models in the speaker profilemay be trained using speech patterns of the CA 118.

In some embodiments, being speaker-dependent with respect to the CA 118does not indicate that the first ASR system 120 a cannot transcribeaudio from other speakers. Rather, the first ASR system 120 a beingspeaker-dependent with respect to the CA 118 may indicate that the firstASR system 120 a may include models that are specifically trained usingspeech patterns of the CA 118 such that the first ASR system 120 a maygenerate transcriptions of audio from the CA 118 with accuracy that maybe improved as compared to the accuracy of transcription of audio fromother people.

The second ASR system 120 b and the third ASR system 120 c may bespeaker-independent. In some embodiments, the second ASR system 120 band the third ASR system 120 c may include analogous or the same modulesthat may be trained using similar or the same speech patterns and/ormethods. Alternatively or additionally, the second ASR system 120 b andthe third ASR system 120 c may include different modules that may betrained using some or all different speech patterns. Additionally oralternatively, two or more ASR systems 120 may use substantially thesame software or may have software modules in common, but use differentASR models.

In some embodiments, the second ASR system 120 b may be configured toreceive the revoiced audio from the CA client 122. The third ASR system120 c may be configured to receive the regular audio from thetranscription unit 114.

The ASR systems 120 may be configured to generate transcriptions of theaudio that each of the ASR systems 120 obtain. For example, the firstASR system 120 a may be configured to generate a first transcriptionfrom the revoiced audio using the speaker-dependent configuration basedon the CA profile. The second ASR system 120 b may be configured togenerate a second transcription from the revoiced audio using aspeaker-independent configuration. The third ASR system 120 c may beconfigured to generate a third transcription from the regular audiousing a speaker-independent configuration. A discussion of how the ASRsystems 120 may generate the transcriptions from the audio is providedlater.

The first ASR system 120 a may be configured to provide the firsttranscription to the fuser 124. The second ASR system 120 b may beconfigured to provide the second transcription to a text editor 126 ofthe CA client 122. The third ASR system 120 c may be configured toprovide the third transcription to the fuser 124. In some embodiments,the fuser 124 may also provide a transcription to the text editor 126 ofthe CA client 122.

The text editor 126 may be configured to obtain transcriptions from theASR systems 120 and/or the fuser. For example, the text editor 126 mayobtain the transcription from the second ASR system 120 b. The texteditor 126 may be configured to obtain edits to a transcription.

For example, the text editor 126 may be configured to direct a displayof a device associated with the CA client 122 to present a transcriptionfor viewing by a person, such as the CA 118 or another CA, among others.The person may review the transcription and provide input through aninput device regarding edits to the transcription.

In some embodiments, the person may also listen to the audio. Forexample, the person may be the CA 118. In these and other embodiments,the person may listen to the audio as the person re-speaks the wordsfrom the audio. Alternatively or additionally, the person may listen tothe audio without re-speaking the words. In these and other embodiments,the person may have context of the communication session by listening tothe audio and thus may be able to make better informed decisionsregarding edits to the transcription.

In some embodiments, the text editor 126 may be configured to edit atranscription based on the input obtained from the person and providethe edited transcription to the fuser 124. Alternatively oradditionally, the text editor 126 may be configured to provide an editedtranscriptions to the transcription system 108 for providing to one orboth of the first device 104 and the second device 106. Alternatively oradditionally, the text editor 126 may be configured to provide the editsto the transcription unit 114 and/or the transcription system 108. Inthese and other embodiments, the transcription unit 114 and/or thetranscription system 108 may be configured to generate the editedtranscription and provide the edited transcription to the fuser 124.

In some embodiments, the transcription may not have been provided to oneor both of the first device 104 and the second device 106 before thetext editor 126 made edits to the transcription. Alternatively oradditionally, the transcription may be provided to one or both of thefirst device 104 and the second device 106 before the text editor 126 isconfigured to edit the transcription. In these and other embodiments,the transcription system 108 may provide the edits or portions of thetranscription with edits to one or both of the first device 104 and thesecond device 106 for updating the transcription on one or both of thefirst device 104 and the second device 106.

The fuser 124 may be configured to obtain multiple transcriptions. Forexample, the fuser 124 may obtain the first transcription, the secondtranscription, and the third transcription. The second transcription maybe obtained from the text editor 126 after edits have been made to thesecond transcription or from the second ASR system 120 b.

In some embodiments, the fuser 124 may be configured to combine multipletranscriptions into a single fused transcription. Embodiments discussedwith respect to FIGS. 13-17 may utilize various methods in which thefuser 124 may operate. In some embodiments, the fuser 124 may providethe fused transcription to the transcription system 108 for providing toone or both of the first device 104 and the second device 106.Alternatively or additionally, the fuser 124 may provide the fusedtranscription to the text editor 126. In these and other embodiments,the text editor 126 may direct presentation of the fused transcription,obtain input, and make edits to the fused transcription based on theinput.

An example of the operation of the environment 100 is now provided. Acommunication session between the first device 104 and the second device106 may be established. As part of the communication session, audio maybe obtained by the first device 104 that originates at the second device106 based on voiced speech of the second user 112. The first device 104may provide the audio to the transcription system 108 over the network102.

The transcription system 108 may provide the audio to the transcriptionunit 114. The transcription unit 114 may provide the audio to the thirdASR system 120 c and the CA client 122. The CA client 122 may directbroadcasting of the audio to the CA 118 for revoicing of the audio. TheCA client 122 may obtain revoiced audio from a microphone that capturesthe words spoken by the CA 118 that are included in the audio. Therevoiced audio may be provided to the first ASR system 120 a and thesecond ASR system 120 b.

The first ASR system 120 a may generate a first transcription based onthe revoiced audio. The second ASR system 120 b may generate a secondtranscription based on the revoiced audio. The third ASR system 120 cmay generate a third transcription based on the regular audio. The firstASR system 120 a and the third ASR system 120 c may provide the firstand third transcriptions to the fuser 124. The second ASR system 120 bmay provide the second transcription to the text editor 126. The texteditor 126 may direct presentation of the second transcription andobtain input regarding edits of the second transcription. The texteditor 126 may provide the edited second transcription to the fuser 124.

The fuser 124 may combine the multiple transcriptions into a singlefused transcription. The fused transcription may be provided to thetranscription system 108 for providing to the first device 104. Thefirst device 104 may be configured to present the fused transcription tothe first user 110 to assist the first user 110 in understanding theaudio of the communication session.

In some embodiments, the fuser 124 may also be configured to provide thefused transcription to the text editor 126. The text editor 126 maydirect presentation of the transcription of the fused transcription tothe CA 118. The CA 118 may provide edits to the fused transcription thatare provided to the text editor 126. The edits to the fusedtranscription may be provided to the first device 104 for presentationby the first device 104.

As described, the generation of the fused transcription may occur inreal-time or substantially real-time continually or mostly continuallyduring the communication sessions. In these and other embodiments, insubstantially real-time may include the fused transcription beingpresented by the first device 104 within one, two, three, five, ten,twenty, or some number of seconds after presentation of the audio by thefirst device 104 that corresponds to the fused transcription.

In some embodiments, transcriptions may be presented on a display of thefirst device 104 after the corresponding audio may be received from thesecond device 106 and broadcast by the first device 104, due to timerequired for revoicing, speech recognition, and other processing andtransmission delays. In these and other embodiments, the broadcasting ofthe audio to the first user 110 may be delayed such that the audio ismore closely synchronized with the transcription from the transcriptionsystem 108 of the audio. In other words, the audio of the communicationsession of the second user 112 may be delayed by an amount of time sothat the audio is broadcast by the first user 110 at about the same timeas, or at some particular amount of time (e.g., 1-2 seconds) before orafter, a transcription of the audio is obtained by the first device 104from the transcription system 108.

In some embodiments, first device 104 may be configured to delaybroadcasting of the audio of the second device 106 so that the audio ismore closely synchronized with the corresponding transcription.Alternatively or additionally, the transcription system 108 or thetranscription unit 114 may delay sending audio to the first device 104.In these and other embodiments, the first device 104 may broadcast audiofor the first user 110 that is obtained from the transcription system108. For example, the second device 106 may provide the audio to thetranscription system 108 or the first device 104 may relay the audiofrom the second device 106 to the transcription system 108. Thetranscription system 108 may delay sending the audio to the first device104. After obtaining the audio from the transcription system 108, thefirst device 104 may broadcast the audio.

In some embodiments, the transcription may also be delayed at selectedtimes to account for variations in latency between the audio and thetranscription. In these and other embodiments, the first user 110 mayhave an option to choose a setting to turn off delay or to adjust delayto obtain a desired degree of latency between the audio heard by thefirst user 110 and the display of the transcription. In someembodiments, the delay may be constant and may be based on a settingassociated with the first user 110. Additionally or alternatively, thedelay may be determined from a combination of a setting and theestimated latency between audio heard by the first user 110 and thedisplay of an associated transcription.

In some embodiments, the transcription unit 114 may be configured todetermine latency by generating a data structure containing endpoints.An “endpoint,” as used herein, may refer to the times of occurrence inthe audio stream for the start and/or end of a word or phrase. In somecases, endpoints may mark the start and/or end of each phoneme or othersub-word unit. A delay time, or latency, may be determined by thetranscription unit 114 by subtracting endpoint times in the audio streamfor one or more words, as determined by an ASR system, from the timesthat the corresponding one or more words appear at the output of thetranscription unit 114 or on the display of the first device 104.

The transcription unit 114 may also be configured to measure latencywithin the environment 100 such as average latency of a transcriptionservice, average ASR latency, average CA latency, or average latency ofvarious forms of the transcription unit 114 and may be incorporated intoaccuracy measurement systems such as described below with reference toFIGS. 44-57. Latency may be measured, for example, by comparing the timewhen words are presented in a transcription to the time when thecorresponding words are spoken and may be averaged over multiple wordsin a transcription, either automatically, manually, or a combination ofautomatically and manually. In some embodiments, audio may be delayed sothat the average time difference from the start of a word in the audiostream to the point where the corresponding word in the transcription ispresented on the display associated with a user corresponds to theuser's chosen setting.

In some embodiments, audio delay and transcription delay may beconstant. Additionally or alternatively, audio delay and transcriptiondelay may be variable and responsive to the audio signal and the timethat portions of the transcription become available. For example, delaysmay be set so that words of the transcription appear on the screen attime periods that approximately overlap the time periods when the wordsare broadcast by the audio so that the first user 110 hears them.Synchronization between audio and transcriptions may be based on wordsor word strings such as a series of a select number of words orlinguistic phrases, with words or word strings being presented on adisplay approximately simultaneously. The various audio vs.transcription delay and latency options described above may be fixed,configurable by a representative of the transcription system 108 such asan installer or customer care agent, or the options may be userconfigurable.

In some embodiments, latency or delay may be set automatically based onknowledge of the first user 110. For example, when the first user 110 isor appears to be lightly hearing impaired, latency may be reduced sothat there is a relatively close synchronization between the audio thatis broadcast and the presentation of a corresponding transcription. Whenthe first user 110 is or appears to be severely hearing impaired,latency may be increased. Increasing latency may give the transcriptionsystem 108 additional time to generate the transcription. Additionaltime to generate the transcription may result in higher accuracy of thetranscription. Alternatively or additionally, additional time togenerate the transcription may result in fewer corrections of thetranscription being provided to the first device 104. A user's level andtype of hearing impairment may be based on a user profile or preferencesettings, medical record, account record, evidence from a camera thatsees the first user 110 is diligently reading the text transcription, orbased on analysis of the first user's voice or on analysis of the firstuser's conversations.

In some embodiments, an ASR system within the transcription system 108may be configured for reduced latency or increased latency. In someembodiments, increasing the latency of an ASR system may increase theaccuracy of the ASR system. Alternatively or additionally, decreasingthe latency of the ASR system may decrease the accuracy of the ASRsystem.

For example, one or more of the ASR systems 120 in the transcriptionunit 114 may include different latencies. As a result, the ASR systems120 may have different accuracies. For example, the first ASR system 120a may be speaker-dependent based on using the CA profile. Furthermore,the first ASR system 120 a may use revoiced audio from the CA client122. As a result, the first ASR system 120 a may be determined, based onanalytics or selection by a user or operator of the transcription system108, to generate transcriptions that are more accurate thantranscriptions generated by the other ASR systems 120. Alternatively oradditionally, the first ASR system 120 a may include configurationsettings that may increase accuracy at the expense of increasinglatency.

In some embodiments, the third ASR system 120 c may generate atranscription faster than the first ASR system 120 a and the second ASRsystem 120 b. For example, the third ASR system 120 c may generate thetranscription based on the audio from the transcription system 108 andnot the revoiced audio. Without the delay caused by the revoicing of theaudio, the third ASR system 120 c may generate a transcription in lesstime than the first ASR system 120 a and the second ASR system 120 b.Alternatively or additionally, the third ASR system 120 c may includeconfiguration settings that may decrease latency.

In these and other embodiments, the third transcription from the thirdASR system 120 c may be provided to the fuser 124 and the transcriptionsystem 108 for sending to the first device 104 for presentation. Thefirst ASR system 120 a and the second ASR system 120 b may also beconfigured to provide the first transcription and the secondtranscription to the fuser 124.

In some embodiments, the fuser 124 may compare the third transcriptionwith the combination of the first transcription and the secondtranscription. The fuser 124 may compare the third transcription withthe combination of the first transcription and the second transcriptionwhile the third transcription is being presented by the first device104.

Alternatively or additionally, the fuser 124 may compare the thirdtranscription with each of the first transcription and the secondtranscription. Alternatively or additionally, the fuser 124 may comparethe third transcription with the combination of the first transcription,the second transcription, and the third transcription. Alternatively oradditionally, the fuser 124 may compare the third transcription with oneof the first transcription and the second transcription. Alternativelyor additionally, in these and other embodiments, the text editor 126 maybe used to edit the first transcription, the second transcription, thecombination of the first transcription, the second transcription, and/orthe third transcription based on input from the CA 118 before beingprovided to the fuser 124.

Differences determined by the fuser 124 may be determined to be errorsin the third transcription. Corrections of the errors may be provided tothe first device 104 for correcting the third transcription beingpresented by the first device 104. Corrections may be marked in thepresentation by the first device 104 in any manner of suitable methodsincluding, but not limited to, highlighting, changing the font, orchanging the brightness of the text that is replaced.

By generating the third transcription faster than other transcriptionsand providing the third transcription to the first device 104 beforefusing or corrections are determined for the third transcription, atranscription may be provided to the first device 104 quicker than inother embodiments. By providing the transcription quicker, the delaybetween the broadcast audio and the presentation of the correspondingtranscription may be reduced. The comparison between the thirdtranscription and one or more of the other transcriptions as describedprovides for corrections to be made of the third transcription such thata more accurate transcription may be presented.

Modifications, additions, or omissions may be made to the environment100 and/or the components operating in the environment 100 withoutdeparting from the scope of the present disclosure. For example, in someembodiments, providing the transcriptions by the transcription system108 may be described as a transcription service. In these and otherembodiments, a person that receives the transcriptions through a deviceassociated with the user, such as the first user 110, may be denoted as“a subscriber” of the transcription system 108 or a transcriptionservice provided by the transcription system 108. In these and otherembodiments, a person whose speech is transcribed, such as the seconduser 112, may be described as the person being transcribed. In these andother embodiments, the person whose speech is transcribed may bereferred to as the “transcription party.”

In these and other embodiments, the transcription system 108 maymaintain a configuration service for devices associated with thetranscription service provided by the transcription system 108. Theconfiguration services may include configuration values, subscriberpreferences, and subscriber information for each device. The subscriberinformation for each device may include mailing and billing address,email, contact lists, font size, time zone, spoken language, authorizedtranscription users, default to captioning on or off, a subscriberpreference for transcription using an automatic speech recognitionsystem or revoicing system, and a subscriber preference for the type oftranscription service to use. The type of transcription service mayinclude transcription only on a specific phone, across multiple devices,using a specific automatic speech recognition system, using a revoicingsystems, a free version of the service, and a paid version of theservice, among others.

In some embodiments, the configuration service may be configured toallow the subscriber to create, examine, update, delete, or otherwisemaintain a voiceprint. In some embodiments, the configuration servicemay include a business server, a user profile system, and a subscribermanagement system. The configuration service may store information onthe individual devices or on a server in the transcription system 108.

In some embodiments, subscribers may access the information associatedwith the configuration services for their account with the transcriptionsystem 108. In these and other embodiments, a subscriber may access theinformation via a device, such as a transcription phone, a smartphone ortablet, by phone, through a web portal, etc. In these and otherembodiments, accessing information associated with the configurationservices for their account may allow a subscriber to modifyconfigurations and settings for the device associated with their accountfrom a remote location. In these and other embodiments, customer ortechnical support of the transcription service may have access todevices of the subscribers to provide technical or service assistance tocustomers when needed. Additionally or alternatively, an imagemanagement service (not shown) may provide storage for images that thesubscriber wishes to display on their associated device. An image may,for example, be assigned to a specific contact, so that when thatcontact name is displayed or during a communication session with thecontact, the image may be displayed. Images may be used to providecustomization to the look and feel of a user interface of a device or toprovide a slideshow functionality. The image management service mayinclude an image management server and an image file server.

As another example, in some embodiments, the transcription system 108may provide transcriptions for both sides of a communication session toone or both of the first device 104 and the second device 106. Forexample, the first device 104 may receive transcriptions of both thefirst audio and the second audio. In these and other embodiments, thefirst device 104 may present the transcriptions of the first audioin-line with the transcriptions from the second audio. In these andother embodiments, each transcription may be tagged, in separate screenfields, or on separate screens to distinguish between thetranscriptions.

Throughout the disclosure, various embodiments may discuss one devicereceiving a transcription for clarity. However, unless noted otherwise,where the disclosure discusses a device receiving a transcription it isto be understood that multiple devices may receive the transcription.Alternatively or additionally, where the disclosure discusses a devicereceiving a transcription of audio from another device it is to beunderstood that the other device may receive a transcription of theaudio from the device. In these and other embodiments, timing messagesmay be sent between the transcription system 108 and either the firstdevice 104 or the second device 106 so that transcriptions may bepresented substantially at the same time on both the first device 104and the second device 106. Alternatively or additionally, thetranscription system 108 may provide a summary of one or both sides ofthe conversation to one or both parties. In these and other embodiments,a device providing audio for transcription may include an interface thatallows a user to modify the transcription. For example, the seconddevice 106 may display transcriptions of audio from the second user 112and may enable the second user 112 to provide input to the second device106 to correct errors in the transcriptions of audio from the seconduser 112. The corrections in the transcriptions of audio from the seconduser 112 may be presented on the first device 104. Alternatively oradditionally, the corrections in the transcriptions of audio from thesecond user 112 may be used for training an ASR system.

As other examples, the first device 104 and/or the second device 106 mayinclude modifications, additions, or omissions. For example, in someembodiments, transcriptions may be transmitted to either one or both ofthe first device 104 and the second device 106 in any format suitablefor either one or both of the first device 104 and the second device 106or any other device to present the transcriptions. For example,formatting may include breaking transcriptions into groups of words tobe presented substantially simultaneously, embedding XML tags, settingfont types and sizes, indicating whether the transcriptions aregenerated via automatic speech recognition systems or revoicing systems,and marking initial transcriptions in a first style and corrections tothe initial transcriptions in a second style, among others.

In some embodiments, the first device 104 may be configured to receiveinput from the first user 110 related to various options available tothe first user 110. For example, the first device 104 may be configuredto provide the options to the first user 110 including turningtranscriptions on or off. Transcriptions may be turned on or off usingselection methods such as: phone buttons, screen taps, soft keys(buttons next to and labeled by the screen), voice commands, signlanguage, smartphone apps, tablet apps, phone calls to a customer careagent to update a profile corresponding to the first user 110, andtouch-tone commands to an IVR system, among others.

In some embodiments, the first device 104 may be configured to obtainand/or present an indication of whether the audio from the communicationsession is being revoiced by a CA. In these and other embodiments,information regarding the CA may be presented by the first device 104.The information may include an identifier and/or location of the CA.Alternatively or additionally, the first device 104 may also presentdetails regarding the ASR system being used. These details may include,but are not limited to the ASR system's vendor, cost, historicalaccuracy, and estimated current accuracy, among others.

In some embodiments, either one or both of the first device 104 and thesecond device 106 may be configured with different capabilities forhelping users with various disabilities and impairments. For example,the first device 104 may be provided with tactile feedback by hapticcontrols such as buttons that vibrate or generate force feedback. Screenprompts and transcription may be audibly provided by the first device104 using text-to-speech or recorded prompts. The recorded prompts maybe sufficiently slow and clear to allow some people to understand theprompts when the people may not understand fast, slurred, noisy,accented, distorted, or other types of less than ideal audio during acommunication session. In some embodiments, transcriptions may bedelivered on a braille display or terminal. The first device 104 may usesensors that detect when pins on a braille terminal are touched toindicate to the second device 106 the point in the transcription wherethe first user 110 is reading. As another example, the first device 104may be controlled by voice commands Voice commands may be useful formobility impaired users among other users.

In some embodiments, either one or both of the first device 104 and thesecond device 106 may be configured to present information related to acommunication session between the first device 104 and the second device106. The information related to a communication session may include:presence of SIT (special information tones), communication sessionprogress tones (e.g. call forwarding, call transfer, forward tovoicemail, dial tone, call waiting, comfort noise, conference calladd/drop and other status tones, network congestion (e.g. ATB),disconnect, three-way calling start/end, on-hold, reorder, busy,ringing, stutter dial tone (e.g. voicemail alert), record tone (e.g.recording alert beeps), etc.), flash hook, on-hold music, an indicatorof when another party answers or disconnects, the number of callingdevices connected to a conference call, an indicator of whether theother party is speaking or silent, and messages relating to thepresence, nature of, and identity of non-speech sounds. Non-speechsounds may include noise, dog barks, crying, sneezing, sniffing,laughing, thumps, wind, microphone pops, car sounds, traffic, multiplepeople talking, clatter from dishes, sirens, doors opening and closing,music, background noise consistent with a specified communicationnetwork such as the telephone network in a specified region or country,a long-distance network, a type of wireless phone service, etc.

In some embodiments, either one or both of the first device 104 and thesecond device 106 may be configured to present an indication of aquality of a transcription being presented. The quality of thetranscription may include an accuracy percentage. In these and otherembodiments, either one or both of the first device 104 and the seconddevice 106 may be configured to present an indication of theintelligibility of the speech being transcribed so that an associateduser may determine if the speech is of a quality that can be accuratelytranscribed. Additionally or alternatively, either one or both of thefirst device 104 and the second device 106 may also present informationrelated to the sound of the voice such as tone (shouting, whispering),gender (male/female), age (elderly, child), audio channel quality(muffled, echoes, static or other noise, distorted), emotion (excited,angry, sad, happy), pace (fast/slow, pause lengths, rushed), speakerclarity, impairments or dysfluencies (stuttering, slurring, partial orincomplete words), spoken language or accent, volume (loud, quiet,distant), and indicators such as two people speaking at once, singing,nonsense words, and vocalizations such as clicks, puffs of air,expressions such as “aargh,” buzzing lips, etc.

In some embodiments, during or at the end of a communication session,either one or both of the first device 104 and the second device 106 maypresent an invitation for the associated user to provide reviews ontopics such as the quality of service, accuracy, latency, settingsdesired for future communication sessions, willingness to pay, andusefulness. In these and other embodiments, with respect to the firstdevice 104, the first device 104 may collect the user's feedback ordirect the user to a website or phone number. The first device 104 maybe configured to receive input from the first user 110 such that thefirst user 110 may mark words that were transcribed incorrectly, advisethe system of terms such as names that are frequently misrecognized ormisspelled, and input corrections to transcriptions, among other inputfrom the first user 110. In these and other embodiments, user feedbackmay be used to improve accuracy, such as by correcting errors in dataused to train or adapt models, correcting word pronunciation, and incorrecting spelling for homonyms such as names that may have variousspellings, among others.

In some embodiments, either one or both of the first device 104 and thesecond device 106 may be configured to display a selected messagebefore, during, or after transcriptions are received from thetranscription system 108. For example, the display showingtranscriptions may start or end the display of transcriptions with acopyright notice that pertains to the transcription of the audio, suchas “Copyright © <year> <owner>,” where “<year>” is set to the currentyear and <owner> is set to the name of the copyright owner.

In some embodiments, either one or both of the first device 104 and thesecond device 106 may be configured to send or receive text messagesduring a communication session with each other, such as instant message,real-time text (RTT), chatting, or texting over short message servicesor multimedia message services using voice, keyboard, links to atext-enabled phone, smartphone or tablet, or via other input modes. Inthese and other embodiments, either one or both of the first device 104and the second device 106 may be configured to have the messagesdisplayed on a screen or read using text-to-speech. Additionally oralternatively, either one or both of the first device 104 and the seconddevice 106 may be configured to send or receive text messages to and/orfrom other communication devices and to and/or from parties outside of acurrent communication. Additionally or alternatively, either one or bothof the first device 104 and the second device 106 may be configured toprovide features such as voicemail, voicemail transcription, speed dial,name dialing, redial, incoming or outgoing communication sessionhistory, and callback, among other features that may be used forcommunication sessions.

In some embodiments, transcriptions may be presented on devices otherthan either one or both of the first device 104 and the second device106. In these and other embodiments, a separate device may be configuredto communicate with the first device 104 and receive the transcriptionsfrom the first device 104 or directly from the transcription system 108.For example, if the first device 104 includes a cordless handset or aspeakerphone feature, the first user 110 may carry the cordless handsetto another location and still view transcriptions on a personalcomputer, tablet, smartphone, cell phone, projector, or any electronicdevice with a screen capable of obtaining and presenting thetranscriptions. Additionally or alternatively, this separate display mayincorporate voice functions so as to be configured to allow a user tocontrol the transcriptions as described in this disclosure.

In some embodiments, the first device 104 may be configured to controlthe transcriptions displayed on a separate device. For example, thefirst device 104 may include control capabilities including, capabilityto select preferences, turn captioning on/off, and select betweenautomatic speech recognition systems or revoicing systems fortranscription generation, among other features.

As another example, the transcription unit 114 may includemodifications, additions, or omissions. For example, in someembodiments, the transcription unit 114 may utilize additional ASRsystems. For example, the transcription unit 114 may provide audio,either revoiced or otherwise, to a fourth ASR system outside of thetranscription system 108 and/or to an ASR service. In these and otherembodiments, the transcription unit 114 may obtain the transcriptionsfrom the fourth ASR system and/or the ASR service. The transcriptionunit 114 may provide the transcriptions to the fuser 124.

In some embodiments, a fourth ASR system may be operating on a devicecoupled to the transcription system 108 through the network 102 and/orone of the other first device 104 and the second device 106.Alternatively or additionally, the fourth ASR system may be included inthe first device 104 and/or the second device 106.

As another example, the transcription unit 114 may not include the oneor more of the fuser 124, the text editor 126, the first ASR system 120a, the second ASR system 120 b, and the third ASR system 120 c. Forexample, in some embodiments, the transcription unit 114 may include thefirst ASR system 120 a, the third ASR system 120 c, and the fuser 124.Additional configurations of the transcription unit 114 are brieflyenumerated here in Table 1, and described in greater detail below.

TABLE 1 1. A CA client. This arrangement may include an ASR system 120transcribing audio that is revoiced by a CA. The ASR system 120 may beadapted to one or more voices. For example, the ASR system 120 may beadapted to the CA's voice, trained on multiple communication sessionvoices, or trained on multiple CA voices. see FIG. 9). 2. One or more CAclients. The CA clients may be arranged in series (e.g.. FIG. 50) or inparallel (e.g., FIG. 52). A fuser 124 may create a consensustranscription. 3. A CA client associated with a CA with special skills,such as a particular spoken language, knowledge of one or more topics,or advanced experience in captioning (i.e., a CA manager or supervisor).4. An ASR system 120 receiving communication session audio. The ASRsystem may run on a variety of devices at various locations. Forexample, the ASR system 120 may run in one or more of severalconfigurations, including with various models and parameter settings andconfigurations supporting one or more of various spoken languages. Insome embodiments, the ASR system 120 may be an ASR system provided byany of various vendors, each with a different cost, accuracy fordifferent types of input, and overall accuracy. Additionally oralternatively, multiple ASR systems 120 may be fused together using afuser. 5. One or more ASR systems 120 whose output is corrected througha text editor of a CA client (see FIG. 31). 6. One or more ASR systems120 operating in parallel with one or more CA clients, the output beingfused to generate a transcription (see FIGS. 32a and 32b). One or moreof the ASR systems 120 may be configured to transcribe communicationsession audio, and one or more ASR systems 120 may transcribe revoicedaudio. 7. Multiple clusters of one or more ASR systems 120, and aselector configured to select a cluster based on load capacity, cost,response time, spoken language, availability of the clusters, etc. 8. Arevoiced ASR system 120 supervised by a non-revoiced ASR system 120configured as an accuracy monitor. The accuracy monitor may report apotential error in real time so that a CA may correct the error.Additionally or alternatively, the accuracy monitor may correct theerror (see FIG. 45). 9. A CA client generating a transcription via aninput device (e.g., keyboard, mouse, touch screen, stenotype, etc.). ACA 118 through the CA client may use a stenotype in some embodimentsrequiring a higher-accuracy transcription. 10. Various combinations ofitems in this table at various times during the course of acommunication session. For example, a first portion of the communicationsession may be transcribed by a first configuration such as an ASRsystem 120 with a CA client correcting errors, and a second portion ofthe communication session may be transcribed by a second configurationsuch as an ASR system 120 using revoiced audio and an ASR system 120using regular audio working in parallel and with fused outputs. 11. Arepeated communication session detector. The repeated communicationsession detector may include an ASR system 120 and a memory storagedevice and may be configured to detect an input sample, such as arecorded audio sample, that has been previously received by thecaptioning system. The detection process may include matching audiosamples, video samples, spectrograms, phone numbers, and/or transcribedtext between the current communication session and one or more previouscommunication sessions or portions of communication sessions. Thedetection process may further use a confidence score or accuracyestimate from an ASR system. The detection process may further use phonenumbers or other device identifiers of one or more communication sessionparties to guide the process of matching and of searching for previousmatching samples. For example, a phone number known to connect to an IVRsystem may prompt the detection process to look for familiar audiopatterns belonging to the IVR system prompts. Once a matching previouscommunication session or portion of a previous communication session hasbeen detected, a transcription or a portion of a transcription of theprevious communication session may be used as a candidate transcriptionof the current communication session. In some embodiments, the candidatetranscription may be used to caption at least part of the currentcommunication session. The ASR system 120 may be used to confirm thatthe candidate transcription continues to match the audio of the currentcommunication session. The ASR system 120 may use a grammar derived fromthe candidate transcription or previous communication session as alanguage model. If the match fails, a different configuration for thetranscription unit 114 may be used to generate a transcription of thecommunication session. In another embodiment, the candidatetranscription may be provided as an input hypothesis to a fuser such asthe fuser 124 described in FIG. 1. 12. Offline transcription, wherecommunication session audio is stored and transcribed after thecommunication session ends.

In some embodiments, the first device 104 and/or the transcriptionsystem 108 may determine which ASR system 120 in the transcription unit114 may be used to generate a transcription to send to the first device104. Alternatively or additionally, the first device 104 and/or thetranscription system 108 may determine whether revoiced audio may beused to determine the transcriptions. In some embodiments, the firstdevice 104 and/or the transcription system 108 may determine which ASRsystem 120 to use or whether to use revoiced audio based on input fromthe first user 110, preferences of the first user 110, an account typeof the first user 110 with respect to the transcription system 108,input from the CA 118, or a type of the communication session, amongother criteria. In some embodiments, the first user 110 preferences maybe set prior to the communication session. In some embodiments, thefirst user may indicate a preference for which ASR system 120 to use andmay change the preference during a communication session.

As another example, the transcription system 108 may includemodifications, additions, or omissions. For example, the transcriptionsystem 108 may include multiple transcription units, such as thetranscription unit 114. Each or some number of the multipletranscription units may include different configurations as discussedabove. In some embodiments, the transcription units may share ASRsystems and/or ASR resources. For example, the third ASR system 120 c orASR services may be shared among multiple different ASR systems. Inthese and other embodiments, the transcription system 108 may beconfigured to select among the transcription units 114 when audio of acommunication session is received for transcription.

In some embodiments, the selection of a transcription unit may depend onavailability of the transcription units. For example, in response to ASRresources for one or more transcription units being unavailable, theaudio may be directed to a different transcription unit that isavailable. In some embodiments, ASR resources may be unavailable, forexample, when the transcription unit relies on ASR services to obtain atranscription of the audio.

In response to multiple transcription units having varying availableresources, audio may be directed to one or more of the transcriptionunits using allocation rules such as (a) allocating audio to resourcesbased on the capacity of each resource, (b) directing audio to one ormore transcription unit resources in priority order, for example bydirecting to a first resource until the first resource is at capacity orunavailable, then to a second resource, and so on, (c) directingcommunication sessions to various transcription units based onperformance criteria such as accuracy, latency, and reliability, (d)allocating communication sessions to various transcription units basedon cost (see #12, #19-21, and #24-29 in Table 2), (e) allocatingcommunication sessions based on contractual agreement, such as withservice providers, (f) allocating communication sessions based ondistance or latency (see #40 in Table 2), and (g) allocatingcommunication sessions based on observed failures such as errormessages, incomplete transcriptions, loss of network connection, APIproblems, and unexpected behavior. The above rules may also be appliedto selecting between resources within a single transcription unit.

In some embodiments, an audio sample may be sent to multipletranscription units and the resulting transcriptions generated by thetranscription units may be combined, such as via fusion. Alternativelyor additionally, one of the resulting transcriptions from one of thetranscription units may be selected to be provided to the first device104. The transcriptions may be selected based on the speed of generatingthe transcription, cost, estimated accuracy, and an analysis of thetranscriptions, among others.

FIG. 2 illustrates another example environment 200 for transcription ofcommunications. The environment 200 may include the network 102, thefirst device 104, and the second device 106 of FIG. 1. The environment200 may also include a transcription system 208. The transcriptionsystem 208 may be configured in a similar manner as the transcriptionsystem 108 of FIG. 1. However, the transcription system 208 of FIG. 2may include additional details regarding the transcription system 208and connecting the first device 104 with an available transcription unit214.

The transcription system 208 may include an automatic communicationsession distributor (ACD) 202. The ACD 202 may include a session bordercontroller 206, a database 209, a process controller 210, and a holdserver 212. The transcription system 208 may further include multipletranscription units 214, including a first transcription unit (TU1) 214a, a second transcription unit (TU2) 214 b, a third transcription unitTU3 214 c, and a fourth transcription unit TU4 214 d. Each of thetranscription units 214 may be configured in a manner as described withrespect to the transcription unit 114 of FIG. 1. In some embodiments,the transcription units 214 may be located in the same or differentlocations. Alternatively or additionally, the CAs associated with CAclients of one or more of the transcription units 214 may be located inthe same or different locations than the transcription units 214.Alternatively or additionally, the CAs associated with CA clients of oneor more of the transcription units 214 may be in the same or differentlocations. In general, the ACD 202 may be configured to select one ofthe transcription units 214 for generating a transcription of audioprovided by the first device 104.

In some embodiments, the first device 104 is configured to communicatewith an ACD 202 over the network 102 and request a transcription ofaudio. After establishing communication with the ACD 202, the firstdevice 104 is configured to register with the session border controller206. The session border controller 206 may record the registration in auser queue in the database 209. Generally, the use of the term databasemay refer to any storage device and not a device with any particularstructure or interface.

Transcription units 214 that are also available to generatetranscriptions may be registered with the session border controller 206.For example, after a transcription unit 214 stops receiving audio at thetermination of a communication session, the transcription unit 214 mayprovide an indication of availability to the session border controller206. The session border controller 206 may record the availabletranscription units 214 in an idle unit queue in the database 209.

In some embodiments, the process controller 210 may be configured toselect an available transcription unit 214 from the idle unit queue togenerate transcriptions for audio from a device in the user queue. Asdescribed above, each transcription unit 214 may be configured togenerate transcriptions using regular audio, revoiced audio, or somecombination of regular audio and revoiced audio using speaker-dependent,speaker-independent, or a combination of speaker-dependent andindependent ASR systems. In these and other embodiments, thetranscription system 208 may include transcription units 214 withmultiple different configurations. For example, each of thetranscription units 214 a-214 n may have a different configuration.Alternatively or additionally, some of the transcription units 214 mayhave the same configuration. Alternatively or additionally, thetranscription units 214 may be differentiated based on a CA associatedwith the transcription unit 214 that may assist in generating therevoiced audio for the transcription unit 214. Thus, a configuration ofa transcription unit 214 may be determined based on the CA associatedwith the transcription unit 214.

The process controller 210 may be configured to select a transcriptionunit based on:

-   -   a) ability of CA associated with the transcription unit (e.g.,        the fastest and most accurate available CA may be assigned        first);    -   b) idle time of CA associated with the transcription unit (e.g.,        CAs with the longest idle time may be used first);    -   c) availability of CA associated with the transcription unit        (e.g., transcription requests may be prioritized to        transcription units using revoiced audio over transcription        units using regular audio as long as there are transcription        units using revoiced audio available, leaving aside a small pool        of transcription units using revoiced audio for emergency        communication sessions, otherwise communication sessions may be        directed over to transcription units using regular audio); and    -   d) priority (e.g., high-priority and emergency communication        sessions, see item 76 of Table 5, may go to a transcription unit        using revoiced audio while low-priority voicemail messages may        be directed to a transcription unit using regular audio).

A method implementing a selection process is described below in greaterdetail with reference to FIG. 3. After the process controller 210selects transcription unit 214 for a transcription request, theregistration may be removed from the user queue and the transcriptionunit 214 may be removed from the idle unit queue in the database 209. Insome embodiments, a hold server 212 may be configured to redirect thetranscription request to the selected transcription unit 214. In someembodiments, the redirect may include a session initiation protocol(“SIP”) redirect signal. After the transcription unit 214 completestranscription of the audio or is directed to stop transcription of theaudio, the transcription unit 214 may be registered by the sessionborder controller 206 in the idle unit queue of the database 209.

As discussed, selection of a transcription unit 214 may be based on anability of a CA associated with the transcription unit 214. In someembodiments, profiles of CAs may be maintained in the database 209 thattrack certain metrics related to the performance of a CA to revoiceaudio and/or make corrections to transcriptions generated by an ASRsystem. For example, each profile may include one or more of: levels ofmultiple skills such as speed, accuracy, an ability to revoicecommunication sessions in noise or in other adverse acousticenvironments such as signal dropouts or distortion, proficiency withspecific accents or languages, skill or experience revoicing speech fromspeakers with various types of speech impairments, skill in revoicingspeech from children, an ability to keep up with fast talkers,proficiency in speech associated with specific terms such as medicine,insurance, banking, or law, the ability to understand a particularspeaker or class of speakers such as a particular speaker demographic,and skill in revoicing conversations related to a detected or predictedtopic or topics of the current communication session, among others. Insome embodiments, each profile may include a rating with respect to eachskill.

In some embodiments, the ACD 202 may be configured to automaticallyanalyze a transcription request to determine whether a particular skillmay be advantageous. If a communication session appears likely tobenefit from a CA with a particular skill, the saved CA skill ratings inthe CA profiles may be used in selecting a transcription unit to receivethe communication session. Additionally or alternatively, when a CA isrevoicing or is about to revoice a communication session, the CA's skillratings, combined with other factors such as estimated difficulty intranscribing a user, transcribing a CA, predicted ASR system accuracyfor the speaker which may be based on or include previous ASR systemaccuracy for the speaker, and the CA's estimated performance (includingaccuracy, latency, and other measures) on the current communicationsession, may be used to estimate the performance of the transcriptionunit on the remainder of the communication session. The estimatedperformance may be used by the ACD 202 to determine whether to changethe transcription arrangement, such as to keep the transcription unit onthe communication session or transfer to another transcription unit,which may or not rely totally on revoiced audio.

In some embodiments, the process controller 210 may be configured toselect an available transcription unit 214 from the idle unit queue togenerate transcriptions for audio from a device in the user queue. Atranscription unit may be selected based on projected performances ofthe transcription unit for the audio of the device. The projectedperformance of a transcription unit may be based on the configuration ofthe transcription unit and the abilities of a CA associated with thetranscription unit.

In some embodiments, the transcription units in the idle unit queue maybe revoiced transcription units or non-revoiced transcription units. Therevoiced transcription units may each be associated with a different CA.In some embodiments, the CA may be selected to be associated with aparticular revoiced transcription unit based on the abilities of the CA.Alternatively or additionally, a revoiced transcription unit may becreated with a particular configuration based on the abilities of theCA. In these and other embodiments, when a revoiced transcription unitassociated with a CA is not selected, the associated CA may be assignedor returned to a pool of available CAs and may subsequently be assignedto work on another communication session. The revoiced transcriptionunits may include speaker-independent ASR systems and/orspeaker-dependent ASR systems that are configured based on the speechpatterns of the CAs associated with the revoiced transcription units.

For example, a CA that revoices audio that results in a transcriptionwith a relatively high accuracy rating may revoice audio for atranscription unit 214 configuration without an additional ASR system.In contrast, revoiced audio from a CA with a relatively low accuracyrating may be used in a transcription unit with multiple ASR systems,the transcriptions of which may be fused together (see FIGS. 34-37) tohelp to increase accuracy. In these and other embodiments, theconfiguration of a transcription unit associated with a CA may be basedon the CA's accuracy rating. For example, a CA with a higher accuracyrating may be associated with transcription units or a transcriptionunit configuration that has a lower number of ASR systems. A CA with alower accuracy rating may be associated with transcription units or atranscription unit configuration that has a higher number of ASRsystems. Thus, when a CA is available, a transcription unit may be usedand associated with the CA based on the abilities of the CA.

As another example, transcription units with different configurationsmay be created based on the predicted type of subscribers that may beusing the service. For example, transcription units with configurationsthat are determined to better handle business calls may be used duringthe day and transcription units with configurations that are determinedto better handle personal calls may be used during the evening.

In some embodiments, the transcription units may be implemented bysoftware configured on virtual machines, for example in a cloudframework. In these and other embodiments, the transcription units mayprovision or de-provision as needed. In some embodiments, revoicingtranscription units may be provisioned when a CA is available and notassociated with a transcription unit. For example, when a CA with aparticular ability is available, a transcription unit with aconfiguration suited for the abilities of the CA may be provisioned.When the CA is no longer available, such as at the end of working-shift,the transcription unit may be de-provisioned. Non-revoicingtranscription units may be provisioned based on demand or other needs ofthe transcription system 208.

In some embodiments, it may take time after a resource or instance isprovisioned before it is available to transcribe communication sessionsand to be placed in the idle unit queue. In these and other embodiments,transcription units may be provisioned in advance, based on projectedneed. In particular, the non-revoiced transcription units may beprovisioned in advance based on projected need.

The ACD 202 or other device may manage the number of transcription unitsprovisioned or de-provisioned. In these and other embodiments, the ACD202 may provision or de-provision transcription units based on theavailable transcription units compared to the current or projectedtraffic load, the number of currently provisioned transcription unitscompared to the number of transcription units actively transcribingaudio from a communication session, traffic load, or other operationsmetrics (see Table 2 for a non-exhaustive list of potential operationsmetrics or features).

TABLE 2 1. Current peak communication session traffic load. 2. Currentaverage communication session traffic load. 3. Previous or projectedpeak traffic load or a statistic such as the peak load projected for aperiod of time such as the next m minutes (for example, 10 minutes). 4.Previous or projected average traffic load or a statistic such as theaverage load over a period of time such as the previous m minutes. 5.The number of revoiced transcription units projected to be available andan estimate for when they will be available. The projection may be basedon information from a scheduling sy stem that tracks anticipated sign-on and sign-off times for individual CAs. Additionally or alternatively,the projection may be based on current revoiced transcription unitavailability. 6. Projected excess revoiced transcription unit capacityover a given period of time. 7. The current number or percentage of idleor available revoiced transcription units. The system may, for examplebe configured to (a) use the available revoiced transcription unitnumber as a feature in selecting between a non- revoiced transcriptionunit or a revoiced transcription unit or (b) send all communicationsessions to revoiced transcription units when there are at least some(plus a few extra to handle higher-priority communication sessions)revoiced transcription units available. 8. The number of idle oravailable revoiced transcription units, averaged over a preceding periodof time. 9. The minimum number of idle revoiced transcription units thatshould, according to operations policies, be available to handlecontingencies such as traffic spikes. 10. The average or longestrevoiced transcription unit idle time. 11. The number of available ASRsystems or ASR ports. Where multiple clusters of ASR system, such asgroups of ASR system from different vendors, are configured, the numberof available ASR systems in each cluster may also be features. If asystem failure such as loss of connectivity or other outage affects thenumber of ASR systems available in a given cluster, the failure may beconsidered in determining availability. These features may be used, forexample, in determining which cluster to use for transcribing a givencommunication session. 12. The number of ASR systems or ASR ports, inaddition to those currently provisioned, that could be provisioned, thecost of provisioning, and the amount of time required for provisioning.13. The skill level of available CAs. This feature may be used to takeCA skill levels into account when deciding whether to use a revoicedtranscription unit for a given communication session. The skill levelmay be used. for example, to preferentially send communication sessionsto revoiced transcription units associated with CAs with stronger orweaker specific skills, skills relevant to the current communicationsession such as spoken language, experience transcribing speakers withimpaired speech, location, or topic familiarity, relatively higher orlower performance scores, more or less seniority, or more or lessexperience. A CA may be assigned to a group of one or more CAs based,for example, on a characteristic relevant to CA skill such as spokenlanguage skill, nationality, location, the location of the CA'scommunication session center, measures of performance such astranscription accuracy, etc. The CA's skill and/or group may be used asa feature by, for example, a. Sending a communication session to a firstgroup when a CA in the first group is available and to a second groupwhen a CA from the first group is not available. b. Selecting atranscription unit configuration (such as a configuration from Table 1)based on the CA's skill or group. For example, a CA with lesser skillsor a lower performance record may be used in a configuration where anASR system provides a relatively greater degree of assistance, comparedto a CA with a greater skill or performance history. In one scenario, atranscription resulting from a revoicing of a poor CA may be fused withtranscriptions from one or more ASR systems whereas a transcription froma better CA may be used without fusion or fused with transcriptions fromrelatively fewer or inferior ASR systems. 14. The number of availablerevoiced transcription units skilled in each spoken language. 15. Theaverage error rate of a revoiced transcription unit pool or group, suchas the pool of available revoiced transcription units or a group ofrevoiced transcription units testing within a selected performancerange. 16. The average latency and error rate across multiple revoicedtranscription units. 17. Projected revoiced transcription unit errorrate. 18. The estimated or projected accuracy of a revoicedtranscription unit on the current communication session. 19. The cost ofan ASR system, such as cost per second or per minute. Multiple ASRresources may be av ailable. in which case, this feature may be the costof each speech recognition resource. 20. The average accuracy, latency,and other performance characteristics of each ASR resource. A resourcemay include ASR on the captioned phone, an ASR server, and ASR cluster,or one or more ASR v endors. 21. In an arrangement including multipleclusters of ASR systems, the load capacity, response time, accuracy,cost, and availability of each cluster. 22. The average accuracy of thecaptioning service, which may take into account revolting accuracy andASR accuracy at its current automation rate. 23. The availability' suchas online status and capacity of various ASR resources. This feature maybe used, for example, in routing traffic away from resources that areoffline and toward resources that are operational and with adequatecapacity. For example, if the captioning service is sending audio to afirst ASR vendor or resource for transcription and the first vendor orresource becomes unavailable, the service may send audio to a second ASRvendor or resource for transcription. 24. The cost of a revokedtranscription unit, such as cost per second or per minute. If revoicedtranscription units have various allocated costs, this cost may be afunction or statistic of a revoked transcription unit's cost structuresuch as the cost of the least expensive available revoiced transcriptionunit. 25. The cost of adding revoked transcription units to thetranscription unit pool. This cost may include a proxy, or allocatedcost, for adding non-standard revoiced transcription units such as CAmanagers, trainers, and QA personnel. 26. The estimated cost of arevoiced transcription unit for the current communication session or theremainder of the current communication session. This cost may beresponsive to the average revoiced transcription unit cost per unit timeand the expected length of the current communication session. 27. Theestimated cost of an ASR system for the current communication session orthe remainder of the current communication session. This cost may beresponsive to the average ASR cost per unit time and the expected lengthof the current communication session. 28. The estimated cost of thecurrent communication session. 29. The cost of captioning communicationsessions currently or averaged over a selected time period. 30.Estimated communication session length. This feature may be based, forexample, on average communication session length of multiple previouscommunication sessions across multiple subscribers and captionedparties. The feature may be based on historical communication sessionlengths averaged across previous communication sessions with the currentsubscriber and/or the current transcription party. 31. The potentialsavings of removing revoiced transcription units from the revoicedtranscription unit pool. 32. The time required to add a revoicedtranscription rmit. 33. The time required to provision an ASR resource.34. The current automation rate, which may be determined as a fractionor percentage of communication sessions connected to ASR rather thanCAs, compared to the total number of communication sessions.Additionally or alternatively, the automation rate may be the number ofASR sessions divided by the number of CA sessions. 35. A businessparameter responsive to the effective or allocated cost of atranscription error. 36. A level of indicated urgency to reduce costs.37. A level of indicated importance to improve service quality. 38.Business objectives, including global metrics, such as the businessobjectives in Table 11. 39. The state of a network connecting acaptioned phone to a rcvoiccd transcription unit or to an ASR system.The state may include indicators for network problems such as lostnetwork connection, missing packets, connection stability, networkbandwidth, latency, WiFi performance at the captioned phone site, anddropouts. This feature may, for example, be used by a captioned phone orcaptioning service to run ASR in the network when the connection is goodand ran ASR on the captioned phone or other local hardware when thephone or service detects network problems. 40. The estimated distance orlatency of a revoiced transcription unit from the captioned phone orfrom the transcription system. One possible use of this feature is toselect from among various ASR vendors, ASR sites, or CA sites based onthe expected round-trip delay in obtaining a transcription from an audiofile. For example, if there are multiple transcription unit sites, atranscription unit site may be selected based on its geographicaldistance, the distance a signal must travel to and from the site, or theexpected time required for a signal to traverse a data network to andfrom the site. In some embodiments, the transcription unit site closestto the captioned phone may be selected. 41. The degree of dialect oraccent similarity between the transcription party and the transcriptionunit site. For example, a transcription unit site may be selected basedon how similar the local dialect or accent of the site is to that of thetranscription party. 42. The account type (See Table 10). 43. Theaverage speed of answer or statistics based on how quickly an availabletranscription unit is attached to a new communication session. 44. Thenumber of missed communication sessions, abandoned communicationsessions, test communication sessions, or communication sessions with noaudio. 45. The number of transcription units and other resources out ofservice. 46. The number, type, and status of operational alarms. 47.Features from Table 5.

For example, if the available transcription unit pool shrinks to aselected level, as determined by the ACD 202, the ACD 202 may configureadditional transcription unit instances so that the additionaltranscription units are ready for possible traffic spikes. Alternativelyor additionally, the ACD 202 may provision a transcription unit and thetranscription unit may provision ASR systems and other resources in thetranscription unit.

In some embodiments, the ACD 202 may also be configured to logcommunication sessions and transcription records in the database 209.Examples of communication session and transcription records include, butare not limited to, phone numbers, date/time, communication sessiondurations, whether communication sessions are transcribed, what portionof communication sessions are transcribed, and whether communicationsessions are revenue-producing (billable), or non-revenue producing(non-billable). The ACD 202 may track whether communication sessions aretranscribed with revoiced or without revoiced audio. Alternatively oradditionally, the ACD 202 may track whether a communication session istranscribed without revoiced audio for a part of the communicationsession and with revoiced audio for another part of the communicationsession. In these and other embodiments, the ACD 202 may indicate whatportion of the communication session was transcribed with revoiced audioand without revoiced audio.

In some embodiments, the ACD 202 may track the transcription for thepurpose of billing a user that requested the transcription. In these andother embodiments, a time of a certain event may be used as the basisfor billing. Examples of time events that may be used as a basis forbilling may include:

-   -   1. The duration of the audio portion of the communication        session, including the time at least one party is connected to        the communication session.    -   2. The duration of the audio portion of the communication        session, including the time at least a subscriber and a        transcription party are on the communication session.    -   3. The duration of the audio portion of the communication        session plus the time used to deliver transcriptions. For        example, after the transcription party stops speaking at the end        of the communication session, there may be an additional period        of time until all transcriptions are delivered to the        subscriber. This time used to deliver transcriptions may be        included in the billed time. In some embodiments, the time used        to deliver transcriptions may include time to present        transcriptions to a display. Additionally or alternatively, the        time used to deliver transcriptions may include time to deliver        transcriptions to a storage location such as the subscriber's        screen buffer or a record of one or more previous communication        sessions.

In some embodiments, the transcription system 208 may include a remotemonitor 224. In these and other embodiments, a remote monitor 224 mayenable a supervisor (e.g., a computer program such as a CA activitymonitor 3104 to be described with reference to FIG. 31, a CA manager, aCA trainer, or quality assurance person) to remotely observe atranscription process. In some embodiments, the remote monitor 224 maybe configured to obtain the audio of the communication session beingtranscribed by the CA. In these and other embodiments, the remotemonitor 224 may direct a device associated with the supervisor tobroadcast the audio for the supervisor to hear. Alternatively oradditionally, the remote monitor 224 may be configured to obtain atranscription based on revoiced audio and edits to a transcription basedon inputs from a CA. Alternatively or additionally, the remote monitor224 may direct a device associated with the supervisor to display partor all of the CA's screen, transcription window, and/or transcriptionbeing generated based on the CA's revoiced audio. In some embodiments,the remote monitor 224 may be configured to provide a communicationinterface between a CA's device and the device used by a supervisor. Inthese and other embodiments, the remote monitor may allow the CA'sdevice and the supervisor's device to exchange messages, audio, and/orvideo.

In some embodiments, the remote monitor 224 may also be configured toprovide to a device associated with a supervisor or other qualityassurance person audio and a transcription of the audio generated by atranscription unit 214. For example, the remote monitor 224 may provideto a supervisor regular audio, revoiced audio associated with theregular audio, and transcriptions as generated based on the regularand/or revoiced audio.

In some embodiments, the remote monitor 224 may capture and provide, forpresentation, additional information regarding the transcription system208 and/or the transcription units 114. The information may includemetrics used for selection of a CA, a transcription unit configuration,a CA identifier, CA activity with respect to a text editor, alerts froma CA activity monitor (as will be described below in greater detail withreference to FIG. 31), communication session statistics such ascommunication session duration, a measure of communication time such asthe number of speech or session seconds, the number of communicationsessions, transcriptions that are generated without using revoicedaudio, the amount of time transcriptions are generated using revoicedaudio, estimated accuracy of the transcriptions, estimated communicationsession transcription difficulty, and latency, among others.

In some embodiments, the remote monitor 224 may be, for example,manually activated, or automatically activated in response to an eventsuch as an alert indicating that a CA may be distracted. In these andother embodiments, the remote monitor 224 may be configured to providean interface to a device to allow the device to present and receiveedits of a transcription in addition to the text editor associated withthe transcription unit generating the transcription of the audio.Alternatively or additionally, the remote monitor 224 may be configuredto transfer responsibility from a first device to a second device tobroadcast and capture audio to generate revoiced audio.

Modifications, additions, or omissions may be made to the environment200 and/or the components operating in the environment 200 withoutdeparting from the scope of the present disclosure. For example, in someembodiments, the transcription system 208 may be networked with morethan just the first device 104. Alternatively or additionally, in someembodiments, the environment 200 may not include the remote monitor 224.

FIG. 3 is a flowchart of an example method 300 to select a transcriptionunit in accordance with some embodiments of the present disclosure. Themethod 300 may be arranged in accordance with at least one embodimentdescribed in the present disclosure. The method 300 may be performed, insome embodiments, by a device or system, such as the ACD 202 of FIG. 2,or another device. In these and other embodiments, the method 300 may beperformed based on the execution of instructions stored on one or morenon-transitory computer-readable media. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, or eliminated, depending on the desiredimplementation.

The method 300 may begin at block 302, where a transcription request maybe obtained. For example, an ACD, such as the ACD 202 of FIG. 2, mayobtain the low-priority transcription request. The priority of thetranscription request may be obtained. The transcription request may beof a lower-priority or higher-priority. Examples of lower-prioritytranscription requests may include, transcribing medical or legalrecords, voicemails, generating or labeling training data for trainingautomatic speech recognition models, court reporting, closed captioningTV, movies, and videos, among others. Examples of higher-prioritytranscription requests may include on-going phone calls, video chats,and paid services, among others.

At block 304, the transcription request with its designated priority maybe placed in the request queue.

At block 306, the transcription unit (TU) availability may bedetermined. The transcription unit availability may be determined by theACD. In some embodiments, the ACD may consider various factors todetermine transcription unit availability. The factors may includeprojected peak traffic load or a statistic such as the peak loadprojected for a period of time, projected average traffic load or astatistic such as the average load projected for a next period of time,the number of transcription units projected to be available and anestimate for when the transcription units will be available based oninformation from a scheduling system that tracks anticipated sign-on andsign-off times for transcription units, past or projected excesstranscription unit capacity over a given period of time, the currentnumber or percentage of idle or available transcription units, and thenumber of idle or available transcription units, averaged over apreceding period of time. In these and other embodiments, thetranscription units determined to be available may be revoicedtranscription units. Alternatively or additionally, the transcriptionunits determined to be available may be non-revoiced transcription unitsor a combination of non-revoiced transcription units and revoicedtranscription units.

At block 308, it is determined if the transcription unit availability isabove a particular threshold. If yes, the method proceeds to block 310.If no, the request may remain in a queue until the determination isaffirmative. The value of the particular threshold may be selected basedon the request being a lower-priority request or a higher-priorityrequest. If the request is a higher-priority request, the particularthreshold may be close to zero such that the higher-priority request maybe accepted with a limited delay. If the request is a lower-priorityrequest, the particular threshold may be higher than the particularthreshold for higher-priority requests to reduce the likelihood thatthere are not transcription units available when a higher-priorityrequest is obtained. At block 310, the request may be sent to anavailable transcription unit.

It is understood that, for this and other processes, operations, andmethods disclosed herein, the functions and/or operations performed maybe implemented in differing order. Furthermore, the outlined functionsand operations are only provided as examples, and some of the functionsand operations may be optional, combined into fewer functions andoperations, or expanded into additional functions and operations withoutdetracting from the essence of the disclosed embodiments. For example,in some embodiments, in block 306, the availability of revoicedtranscription units may be measured and the availability may be comparedto a threshold in block 308. When the availability is below thethreshold, the method 300 may return to block 306 and the availabilityof non-revoiced transcription units may be measured and the method 300may proceed to block 308. Thus, in these and other embodiments, themethod 300 may select revoiced transcription units before selectingnon-revoiced transcription units.

FIG. 4 illustrates another example environment 400 for transcription ofcommunications in accordance with some embodiments of the presentdisclosure. The environment 400 may include the network 102, the firstdevice 104, and the second device 106 of FIG. 1. The environment 400 mayalso include a transcription system 408. The transcription system 408may be configured in a similar manner as the transcription system 108 ofFIG. 1. However, the transcription system 408 of FIG. 4 may includeadditional details regarding transferring audio of a communicationsession between transcription units or between ASR systems in atranscription unit.

The transcription system 408 may include an ACD 402 that includes aselector 406. The transcription system 408 may also include a firsttranscription unit 414 a and a second transcription unit 414 b, referredto as the transcription units 414, and an accuracy tester 430. The firsttranscription unit 414 a may include a first ASR system 420 a, a secondASR system 420 b, referred to as the ASR system(s) 420, and a CA client422.

The ACD 402 may be configured to perform the functionality describedwith respect to the ACD 202 of FIG. 2 to select a transcription unit togenerate a transcription of audio of a communication session between thefirst device 104 and the second device 106. After the initial assignmentof the transcription unit 414, the selector 406 of the ACD 402 may beconfigured to change the transcription unit 414 generating thetranscription or a configuration of the transcription unit 414generating the transcription during the communication session. In someembodiments, the selector 406 may change the transcription unit 414 bydirecting the audio to a different transcription unit. Alternatively oradditionally, the selector 406 may change the configuration of thetranscription unit 414 by directing audio to a different ASR system 420within the same transcription unit 414.

In some embodiments, the automated accuracy tester 430 may be configuredto estimate an accuracy of transcriptions generated by the transcriptionunits 414 and/or the ASR systems 420. In these and other embodiments,the accuracy tester 430 may be configured to estimate the quality of thetranscriptions in real-time during the communication session. Thus, theaccuracy tester 430 may generate the estimated accuracy as thetranscriptions are generated and provided to the first device 104. Theaccuracy tester 430 may provide the estimated qualities to the selector406.

In some embodiments, the term “accuracy” may be used generically torefer to one or more metrics of a transcription or of the process ofgenerating a transcription. For example, the term accuracy may representone or more metrics including values or estimates for: accuracy,quality, error counts, accuracy percentages, error rates, error ratepercentages, confidence, likelihood, likelihood ratio, log likelihoodratio, word score, phrase score, probability of an error, wordprobability, quality, and various other metrics related totranscriptions or the generation of transcriptions. Additionally, any ofthe above terms may be used in this disclosure interchangeably unlessnoted otherwise or understood from the context of the description. Forexample, an embodiment that describes the metric of confidence is usedto make a decision or may rely on other of the metrics described aboveto make the decision. Thus, the use of a specific term outside of theterm accuracy should not be limiting, but rather as an example metricthat may be used from multiple potential metrics.

For example, accuracy percentage of a transcription may equal accuracyof tokens in the transcription multiplied by 100% and divided by thenumber of tokens in the transcription. In these and other embodiments,the accuracy percentage may be 100% minus the percentage error rate. Asanother example, accuracy may equal one minus the error rate when errorand accuracy are expressed in decimals Thus, a description forestimating or utilizing one form of accuracy is to be understood to alsobe a description for estimating or utilizing another form of accuracy,since accuracy and error rates are merely different expressions of thesame phenomenon. As another example, an agreement rate may besubstantially equivalent to a disagreement rate, since they arecomplementary. For example, an agreement rate may be expressed as one(or 100%) minus the disagreement rate. In another example, where amethod is described for using an agreement rate to form an estimate orselection, then a disagreement rate may be similarly used.

In some embodiments, the estimated or predicted accuracy may be based onpast accuracy estimates. For example, past accuracy estimates mayinclude the estimated and/or calculated accuracy for a previous periodof time (e.g., for the past 1, 5, 10, 20, 30, or 60 seconds), since thebeginning of the communication session, or during at least part of aprevious communication session with the same transcription party. Inthese and other embodiments, the predicted accuracy may be based on thepast accuracy estimates. Alternatively or additionally, the predictedaccuracy may be the part accuracy estimates. For example, if the pastaccuracy estimates an accuracy of 95%, the predicted accuracy goingforward may equal the past accuracy estimates and may be 95%. Generally,when discussing a predicted accuracy in this disclosure, the predictedaccuracy may be the past accuracy or may be a determination that isbased on the past accuracy. Thus, the use of the term “predict,”“predicted,” or “prediction” does not imply that additional calculationsare performed with respect to previous estimates or determinations ofaccuracy. Additionally, as discussed, the term accuracy may representone or more metrics and the use of the term “predict,” “predicted,” or“prediction” with respect to any metric should be interpreted asdiscussed above. Additionally, the use of the term “predict,”“predicted,” or “prediction” with respect to any quantity, method,variable, or other element in this disclosure should be interpreted asdiscussed above and does not imply that additional calculations areperformed to determine the prediction. For example, where a predictionis described with reference to metrics such as availability oftranscription units, conversation topic, characteristics and types ofusers or CAs, cost of service, traffic volumes, business or operationsmeasures such as a global metric, spoken language, an estimate based onpast or current values may be equivalently used.

In some embodiments, estimated accuracy of transcriptions of audiogenerated by a first transcription unit or ASR system may be based ontranscriptions of the audio generated by a second transcription unit orASR system. In these and other embodiments, the second transcriptionunit or ASR system may operate in one of various operating modes. Thevarious operating modes may include a normal operating mode thatexecutes a majority or all of the features described below with respectto FIG. 5. Another operating mode may include a reduced mode thatconsumes fewer resources as opposed to a normal operating mode. In thereduced mode, the second transcription unit or ASR system may run withsmaller speech models or may execute a subset of the features describedbelow with reference to FIG. 5. In a reduced mode, the secondtranscription unit or ASR system may not necessarily provide afull-quality transcription, but may be used, for example, to estimateaccuracy of another transcription unit and/or ASR system. Other methodsmay be used to estimate the accuracy of transcriptions. Embodimentsdescribing how the accuracy tester 430 may generate the estimatedaccuracy are described later in the disclosure with respect to FIGS.18-29 and 45-59, among others.

In some embodiments, the selector 406 may obtain an estimated accuracyof the transcription units 414 and/or the ASR systems 120 from theaccuracy tester 430. In these and other embodiments, the selector 406may be configured to change the transcription unit 414 generating thetranscription or a configuration of the transcription unit 414generating the transcription during the communication session based onthe estimated accuracy.

In these and other embodiments, the selector 406 may be configured todetermine when the estimated accuracy associated with a first unit notperforming transcriptions, such as the transcription unit 414 or ASRsystem 420, meets an accuracy requirement. When the estimated accuracyassociated with a first unit meets the accuracy requirement, the firstunit may begin performing transcriptions. In these and otherembodiments, a second unit, such as the transcription unit 414 or ASRsystem 420, that previously performed transcriptions when the first unitmeets the accuracy requirement may stop performing transcriptions.

In some embodiments, the accuracy requirement may be associated with aselection threshold value. In these and other embodiments, the selector406 may compare the estimated accuracy of a first unit, such as one ofthe ASR systems 420 or one of the transcription unit 414, to theselection threshold value. When the estimated accuracy is above theselection threshold value, the accuracy requirement may be met and theselector 406 may select the first unit to generate transcriptions. Whenthe estimated accuracy is below the selection threshold value, theaccuracy requirement may not be met and the selector 406 may not selectthe first unit to generate transcriptions. In these and otherembodiments, when the accuracy requirement is not met, the selector 406may continue to have a second unit that previously generatedtranscriptions to continue to generate transcriptions. In these andother embodiments, the selection threshold value may be based onnumerous factors and/or the selection threshold value may be a relativevalue that is based on the accuracy of the ASR system 420 and/or thetranscription unit 414.

For example, in some embodiments, the selection threshold value may bebased on an average accuracy of one or more of the transcription units414 and/or the ASR systems 420. For example, when the selector 406 isselecting between the first transcription unit 414 a and the secondtranscription unit 414 b, an average accuracy of the first transcriptionunit 414 a and an average accuracy of the second transcription unit 414b may be combined. For example, the average accuracies may besubtracted, added using a weighted sum, or averaged. The selectionthreshold value may be based on the average accuracies of thetranscription units 414.

In some embodiments, an average accuracy of the transcription unit 414and/or the ASR system 420 may be determined. The average accuracy may bebased on a comparison of a reference transcription of audio to atranscription of the audio. For example, a reference transcription ofaudio may be generated from the audio. Additionally, the transcriptionunit 414 and/or the ASR system 420 may generate a transcription of theaudio. The transcription generated by the transcription unit 414 and/orthe ASR system 420 and the reference transcription may be compared todetermine the accuracy of the transcription by the transcription unit414 and/or the ASR system 420. The accuracy of the transcription may bereferred to as an average accuracy of the transcription unit 414 and/orthe ASR system 420.

In some embodiments, the reference transcription may be based on audiocollected from a production service that is transcribed offline. Oneexample of transcribing audio offline may include the steps ofconfiguring a transcription management, transcription, and editing toolto (a) send an audio sample to a first transcriber for transcription,then to a second transcriber to check the results of the firsttranscriber, (b) send multiple audio samples to a first transcriber andat least some of the audio samples to a second transcriber to checkquality, or (c) send an audio sample to two or more transcribers and touse a third transcriber to check results when the first two transcribersdiffer. Additionally or alternatively, the accuracy tester 410 maygenerate a reference transcription in real time and automaticallycompare the reference to the hypothesis to determine an error rate inreal time.

In some embodiments, a reference transcription may be generated bysending the same audio segment to multiple different revoicedtranscription units that each transcribe the audio. Alternatively oradditionally, the same audio segment may be sent to multiple differentnon-revoiced transcription units that each transcribe the audio. Theoutput of some or all of the non-revoiced and revoiced transcriptionunits may be provided to a fuser that may combine the transcriptionsinto a reference transcription.

In some embodiments, the accuracy requirement may be associated with anaccuracy margin. In these and other embodiments, the selector 406 maycompare the estimated accuracy of a first unit, such as one of the ASRsystems 420 or one of the transcription units 414, to the estimatedaccuracy of a second unit, such as one of the ASR systems 420 or one ofthe transcription units 414. When the difference between the estimatedaccuracies of the first and second units is less than the accuracymargin, the accuracy requirement may be met and the selector 406 mayselect the first unit to generate transcriptions. When the differencebetween the estimated accuracies of the first and second units is morethan the accuracy margin and the estimated accuracy of the first unit isless than the estimated accuracy of the second unit, the accuracyrequirement may not be met and the second unit may continue to generatetranscriptions.

An example of the operation of the transcription system 408 follows. Insome embodiments, the ACD 402 may initially assign the firsttranscription unit 414 a to generate transcriptions for audio of acommunication session. In these and other embodiments, the selector 406may direct the audio to the first transcription unit 414 a. The firsttranscription unit 414 a may use the first ASR system 420 a and thesecond ASR system 420 b to generate transcriptions. In some embodiments,the first ASR system 420 a may be a revoiced ASR system that usesrevoiced audio based on the audio of the communication session. Therevoiced audio may be generated by the CA client 422. Alternatively oradditionally, the first ASR system 420 a may be speaker-independent orspeaker-dependent. The second ASR system 420 b may use the audio fromthe communication session to generate transcriptions. The secondtranscription unit 414 b may be configured in any manner described inthis disclosure. For example, the second transcription unit 414 b mayinclude an ASR system that is speaker-independent. In some embodiments,the ASR system may be an ASR service that the second transcription unit414 b communicates with through an application programming interface(API) of the ASR service.

The accuracy tester 430 may estimate the accuracy of the firsttranscription unit 414 a based on the transcriptions generated by thefirst ASR system 420 a. The accuracy tester 430 may estimate theaccuracy of the second transcription unit 414 b based on thetranscriptions generated by the second ASR system 420 b. In someembodiments, the transcriptions generated by the second ASR system 420 bmay be fused with the transcriptions generated by the first ASR system420 a. The fused transcription may be provided to the first device 104.

When the difference between the estimated accuracies is less than anaccuracy margin, the selector 406 may direct audio to the secondtranscription unit 414 b. In these and other embodiments, the firsttranscription unit 414 a may stop generating transcriptions and thesecond transcription unit 414 b may generate the transcriptions for thecommunication session.

As an alternative, the second transcription unit 414 b may generatetranscriptions that may be used to estimate the accuracy of the firsttranscription unit 414 a or the second transcription unit 414 b. Thetranscriptions generated by the second transcription unit 414 b may notbe provided to the first device 104. In these and other embodiments, thetranscriptions generated by the second transcription unit 414 b may begenerated by an ASR system operating in a reduced mode.

As another example, the first transcription unit 414 a may use the firstASR system 420 a with the CA client 422 to generate transcriptions tosend to the first device 104. In these and other embodiments, theaccuracy tester 430 may estimate the accuracy of the second ASR system420 b based on the transcriptions generated by the second ASR system 420b.

When the estimated accuracy of the second ASR system 420 b is greaterthan a selection threshold value, the selector 406 may select the secondASR system 420 b to generate transcriptions to send to the first device104. In these and other embodiments, the first ASR system 420 a may stopgenerating transcriptions.

Modifications, additions, or omissions may be made to the environment400 and/or the components operating in the environment 400 withoutdeparting from the scope of the present disclosure. For example, in someembodiments, the transcription system 408 may include additionaltranscription units. In these and other embodiments, the selector 406may be configured with multiple selection threshold values. Each of themultiple selection threshold values may correspond to one of thetranscription units.

As another example, in some embodiments, the ASR systems 420 and the ASRsystems in the second transcription unit 414 b may operate as describedwith respect to FIGS. 5-12 and may be trained as described in FIGS.56-83. In these and other embodiments, the selector 406 and/or theenvironment 400 may be configured in a manner described in FIGS. 18-30which describe various systems and methods that may be used to selectbetween different transcription units. As described with respect to FIG.4 and FIGS. 18-30, selection among transcription units may be based onstatistics with respect to transcriptions of audio generated by ASRsystems. FIGS. 44-55, among others, describe various systems and methodsthat may be used to determine the statistics. In some embodiments, thestatistics may be generated by comparing a reference transcription to ahypothesis transcription. In these and other embodiments, the referencetranscriptions may be generated based on the generation of higheraccuracy transcriptions as described in FIGS. 31-43. The higher accuracytranscriptions as described in FIGS. 31-43 may be generated using thefusion of transcriptions described in FIGS. 13-17. This example providesan illustration regarding how the embodiments described in thisdisclosure may operate together. However, each of the embodimentsdescribed in this disclosure may operate independently and are notlimited to operations and configurations as described with respect tothis example.

Turning now to various embodiments of the present disclosure thatdiscuss automatic speech recognition (“ASR”), FIGS. 5-12 depictembodiments of systems and methods for generating a transcription fromaudio. FIG. 5 is a schematic block diagram illustrating an embodiment ofan environment 500 for speech recognition, arranged in accordance withsome embodiments of the present disclosure.

In some embodiments, the environment 500 may include an ASR system 520,models 530, and model trainers 522. The ASR system 520 may be an exampleof the ASR systems 120 of FIG. 1. The ASR system 520 may include variousblocks including a feature extractor 504, a feature transformer 506, aprobability calculator 508, a decoder 510, a rescorer 512, a grammarengine 514 (to capitalize and punctuate), and a scorer 516. Each of theblocks may be associated with and use a different model from the models530 when performing its particular function in the process of generatinga transcription of audio. In general, the model trainers 522 may usedata 524 to generate the models 530. The models 530 may be used by theblocks in the ASR system 520 to perform the process of generating atranscription of audio.

In some embodiments, the feature extractor 504 receives audio samplesand generates one or more features based on a feature model 505. Typesof features may include LSFs (line spectral frequencies), cepstralfeatures, and MFCCs (Mel Scale Cepstral Coefficients). In someembodiments, audio samples (meaning the amplitudes of a speech waveform,measured at a selected sampling frequency) serve as features. Featuresmay include features derived from a video signal, such as a video of thespeaker's lips or face. For example, an ASR system may use featuresderived from the video signal that indicate lip position or motiontogether with features derived from the audio signal.

In one example, a camera may capture video of a CA's lips or face andforward the signal to the feature extractor 504. In another example,audio and video features may be extracted from a party on a videocommunication session and sent to the feature extractor 504. In anotherexample, lip movement may be used to indicate whether a party isspeaking so that the ASR system 520 may be activated during speech totranscribe the speech. Alternatively or additionally, the ASR system 520may use lip movement in a video to determine when a party is speakingsuch that the ASR system 520 may more accurately distinguish speech fromaudio interference such as noise from sources other than the speaker.

In some embodiments, the feature transformer 506 may be configured toconvert the extracted features, based on a transform model 507, into atransformed format that may provide better accuracy or less centralprocessing unit (CPU) processing. The feature transformer 506 maycompensate for variations in individual voices such as pitch, gender,accent, age, and other individual voice characteristics. The featuretransformer 506 may also compensate for variations in noise, distortion,filtering, and other channel characteristics. The feature transformer506 may convert a feature vector to a vector of a different length toimprove accuracy or reduce computation.

In some embodiments, the feature transformer 506 may bespeaker-independent, meaning that the transform is trained on and usedfor all speakers. Alternatively or additionally, the feature transformer506 may be speaker-dependent, meaning that each speaker or small groupof speakers has an associated transform which is trained on and used forthat speaker or small group of speakers. For example, a machine learner518 (a.k.a. modeling or model training) when creating aspeaker-dependent model may create a different transform for eachspeaker or each device to improve accuracy. Alternatively oradditionally, the feature transformer 506 may create multipletransforms. In these and other embodiments, each speaker or device maybe assigned to a transform. The speaker or device may be assigned to atransform, for example, by trying multiple transforms and selecting thetransform that yields or is estimated to yield the highest accuracy oftranscriptions for audio from the speaker or audio.

One example of a transform may include a matrix which is configured tobe multiplied by a feature vector created by the feature extractor 504.For example, if the feature extractor 504 generates a vector ā of 60features with elements a₁, a₂, a₃, . . . , a₆₀ and the featuretransformer 506 uses a 40×60 matrix T, then the transformed features ō,including elements o₁, o₂, o₃, . . . , o₄₀ are computed as ō=Tā+ū, whereü is a constant and may optionally be zero. In these and otherembodiments, the matrix T and the constant t₁ may be included in thetransform model 507 and may be generated by the machine learner 518using the data 524. Methods for computing a transformation matrix T,such as Maximum Likelihood Linear Regression (MLLR), Constrained MLLR(CMLLR), and Feature-space MLLR (fMLLR), and may be used to generate thetransform model 507 used by the feature transformer 506. As analternative to transforming features, model parameters such as acousticmodel parameters may be adapted to individuals or groups using methodssuch as MAP (maximum a posteriori) adaptation.

In some embodiments, a single transform for all users may be determinedby tuning to, or analyzing, an entire population of users. Additionallyor alternatively, a transform may be created by the feature transformer506 for each speaker or group of speakers, where a transcription partyor all speakers associated with a specific subscriber/user device mayinclude a group, so that the transform adjusts the ASR system for higheraccuracy with the individual speaker or group of speakers. The differenttransforms may be determined using the machine learner 518 and differentdata of the data 524.

The probability calculator 508, in some embodiments, may be configuredto receive a vector of features from the feature transformer 506, and,using an acoustic model 509 (generated by an AM trainer 517), determinea set of probabilities, such as phoneme probabilities. The phonemeprobabilities may indicate the probability that the audio sampledescribed in the vector of features is a particular phoneme of speech.Alternatively or additionally, the phoneme probabilities may includemultiple phonemes of speech that may be described in the vector offeatures. Each of the multiple phonemes may be associated with aprobability that the audio sample includes that particular phoneme. Aphoneme of speech may include any perceptually distinct units of soundthat may be used to distinguish one word from another. The probabilitycalculator 508 may send the phonemes and the phoneme probabilities tothe decoder 510.

In some embodiments, the decoder 510 receives a series of phonemes andtheir associated probabilities. In some embodiments, the phonemes andtheir associated probabilities may be determined at regular intervalssuch as every 5, 7, 10, 15, or 20 milliseconds. In these and otherembodiments, the decoder 510 may also read a language model 511(generated by an LM trainer 519) such as a statistical language model orfinite state grammar and, in some configurations, a pronunciation model513 (generated by a lexicon trainer 521) or lexicon. The decoder 510 maydetermine a sequence of words or other symbols and non-word markersrepresenting events such as laughter or background noise. Additionallyor alternatively, the decoder 510 determines a series of words, denotedas a hypothesis, for use in generating a transcription. In someembodiments, the decoder 510 may output a structure in a rich format,representing multiple hypotheses or alternative transcriptions, such asa word confusion network (WCN), lattice (a connected graph showingpossible word combinations and, in some cases, their associatedprobabilities), or n-best list (a list of hypotheses in descending orderof likelihood, where “n” is the number of hypotheses).

In some embodiments, the rescorer 512 analyzes the multiple hypothesesand reevaluates or reorders them and may consider additional informationsuch as application information or a language model other than thelanguage model used by the decoder 510, such as a rescoring languagemodel. A rescoring language model may, for example, be a neuralnet-based or an n-gram based language model. In some embodiments, theapplication information may include intelligence gained from userpreferences or behaviors, syntax checks, rules pertaining to theparticular domain being discussed, etc.

In some embodiments, the ASR system 520 may have two language models,one for the decoder 510 and one for the rescorer 512. In these and otherembodiments, the model for the decoder 510 may include an n-gram basedlanguage model. The model for the rescorer 512 may include an RNNLM(recurrent neural network language model).

In some embodiments, the decoder 510 may use a first language model thatmay be configured to run quickly or to use memory efficiently such as atrigram model. In these and other embodiments, decoder 510 may renderresults in a rich format and transmit the results to the rescorer 512.The rescorer 512 may use a second language model, such as an RNNLM,6-gram model or other model that covers longer n-grams, to rescore theoutput of the decoder 510 and create a transcription. The first languagemodel may be smaller and may run faster than the second language model.

In some embodiments, the rescorer 512 may be included as part of the ASRsystem 520. Alternatively or additionally, in some embodiments, therescorer 512 may not be included in the ASR system 520 and may beseparate from the ASR system 520, as in FIG. 71.

In some embodiments, part of the ASR system 520 may run on a firstdevice, such as the first device 104 of FIG. 1, that obtains andprovides audio for transcription to a transcription system that includesthe ASR system 520. In these and other embodiments, the remainingportions of the ASR system 520 may run on a separate server in thetranscription system. For example, the feature extractor 504 may run onthe first device and the remaining speech recognition functions may runon the separate server. As another example, the first device may computephoneme probabilities, such as done by the probability calculator 508and may forward the phoneme probabilities to the decoder 510 miming onthe separate server. In yet another example, the feature extractor 504,feature transformer 506, the probability calculator 508, and the decoder510 may run on the first device. In these and other embodiments, alanguage model used by the decoder 510 may be a relatively smalllanguage model, such as a trigram model. In these and other embodiments,the first device may transmit the output of the decoder 510, which mayinclude a rich output such as a lattice, to the separate server. Theseparate server may rescore the results from the first device togenerate a transcription. In these and other embodiments, the rescorer512 may be configured to utilize, for example, a relatively largerlanguage model such as an n-gram language model, where n may be greaterthan three, or a neural network language model. In some embodimentsillustrated herein, the rescorer 512 is illustrated without a model ormodel training, however it is contemplated that the rescorer 512 mayutilize a model such as any of the above described models.

In some embodiments, a first language model may include wordprobabilities such as entries reflecting the probability of a particularword given a set of nearby words. A second language model may includesubword probabilities, where subwords may be phonemes, syllables,characters, or other subword units. The two language models may be usedtogether. For example, the first language model may be used for wordstrings that are known, that are part of a first lexicon, and that haveknown probabilities. For a word that is out-of-vocabulary, such as whenthe word is not part of a first lexicon or does not have a knownprobability in the first language model, the second language model maybe used to estimate probabilities based on subword units. A secondlexicon may be used to identify a word corresponding to the recognizedsubword units.

In some embodiments, the decoder 510 and/or the rescorer 512 may beconfigured to determine capitalization and punctuation. In these andother embodiments, the decoder and/or the rescorer 512 may use thecapitalization and punctuation model 515. Additionally or alternatively,the decoder 510 and/or rescorer 512 may output a string of words whichmay be analyzed by the grammar engine 514 to determine which wordsshould be capitalized and how to add punctuation. The scorer 516 may beconfigured to, once the transcription has been determined, generate anaccuracy estimate, score, or probability regarding whether the words inthe transcription are correct. The accuracy estimate may be generatedbased on a confidence model 523 (generated by a confidence trainer 525).This score may evaluate each word individually or the score may quantifyphrases, sentences, turns, or other segments of a conversation.Additionally or alternatively, the scorer 516 may assign a probabilitybetween zero and one for each word in the transcription and an estimatedaccuracy for the entire transcription.

In some embodiments, the scorer 516 may be configured to transmit thescoring results to a selector, such as the selector 406 of FIG. 4. Theselector may use the scoring to select between transcription unitsand/or ASR systems for generating transcriptions of a communicationsession. The output of the scorer 516 may also be provided to a fuserthat combines transcriptions from multiple sources. In these and otherembodiments, the fuser may use the output of the scorer 516 in theprocess of combining. For example, the fuser may weigh eachtranscription provided as an input by the confidence score of thetranscription. Additionally or alternatively, the scorer 516 may receiveinput from any or all preceding components in the ASR system 520.

In the depicted embodiment, each component in the ASR system 520 may usea model 530, which is created using model trainers 522. Training modelsmay also be referred to as training an ASR system. Training models mayoccur online or on-the-fly (as speech is processed to generatetranscriptions for communication sessions) or offline (processing isperformed in batches on stored data). In some embodiments, models may bespeaker-dependent, in which case there may be one model or set of modelsbuilt for each speaker or group of speakers. Alternatively oradditionally, the models may be speaker-independent, in which case theremay be one model or set of models for all speakers.

ASR system behavior may be tuned by adjusting runtime parameters such asa scale factor that adjusts how much relative weight is given to alanguage model vs. an acoustic model, beam width and a maximum number ofactive arcs in a beam search, timers and thresholds related to silenceand voice activity detection, amplitude normalization options, noisereduction settings, and various speed vs. accuracy adjustments. A set ofone or more runtime parameters may be considered to be a type of model.In some embodiments, an ASR system may be tuned to one or more voices byadjusting runtime parameters to improve accuracy. This tuning may occurduring a communication session, after one or more communication sessionswith a given speaker, or after data from multiple communication sessionswith multiple speakers is collected. Tuning may also be performed on aCA voice over time or at intervals to improve accuracy of aspeaker-independent ASR system that uses revoiced audio from the CA.

The depiction of models 530 is illustrative only. Each model shown maybe a model developed through machine learning, a set of rules (e.g., adictionary), a combination of both, or by other methods. One or morecomponents of the model trainer 522 may be omitted in cases where thecorresponding ASR system 520 components do not use a model. Models 530may be combined with other models to create a new model. The differenttrainers of the model trainer 522 may receive data 524 when creatingmodels.

The depiction of separate components in the ASR system 520 is alsoillustrative. Components may be omitted, combined, replaced, orsupplemented with additional components. For example, a neural net maydetermine the sequence of words directly from features or speechsamples, without a decoder 510, or the neural net may act as a decoder510. In another example, an end-to-end ASR system may include a neuralnetwork or combination of neural networks that receives audio samples asinput and generates text as output. An end-to-end ASR system mayincorporate the capabilities shown in FIG. 5.

One example of an additional component may be a profanity detector (notshown) that filters or alters profanity when detected. The profanitydetector may operate from a list of terms (words or phrases) consideredprofane (including vulgar or otherwise offensive) and, on determiningthat a recognized word matches a term in the list, may (1) delete theterm, (2) change the term to a new form such as retaining the first andlast letter and replacing in-between characters with a symbol such as“-,” (3) compare the confidence of the word or phrase to a selectedthreshold and delete recognized profane terms if the confidence is lowerthan the threshold, or (4) allow the user to add or delete the termto/from the list. An interface to the profanity detector may allow theuser/subscriber to edit the list to add or remove terms and to enable,disable, or alter the behavior of profanity detection.

Alternatively or additionally, profane words may be assigned a lowerprobability or weight in the language model 511 or during ASR or fusionprocessing or may be otherwise treated differently from non-profanewords so that the profane words may be less likely to be falselyrecognized. For example, if the language model 511 includes conditionalprobabilities, such as a numeric entry giving the probability of a wordword3 given the previous n−1 words (e.g., P(word3|word1,word2) wheren=3), then the probability for profane words may be replaced withk*P(word3|word1,word2), where k is a weight used to adjust theprobability of recognition for profanity.

Some terms may be considered offensive only in certain situations, so,in some embodiments, the profanity list may also specify a context, suchas a phrase (which could be a word, series of words, or other constructsuch as a lattice, grammar, or regular expression) that must precede theterm and/or a phrase that must follow the term before it is considered amatch. Alternatively or additionally, the list or context rules may bereplaced by a natural language processor, a set of rules, or a modeltrained on data where profane and innocent terms have been labeled. Inthese and other embodiments, a function may be constructed thatgenerates an output denoting whether the term is likely to be offensive.For example, a profanity detector may learn, by analyzing examples or byreading a model trained on examples of text where profane usage istagged, to distinguish a term used in a profane vs. non-profane context.To better distinguish profanity, the detector may use information suchas the topic of conversation, one or more voice characteristics of thespeaker, including the identity, demographic, pitch, accent, andemotional state, an evaluation of the speaker's face or facialexpression on a video communication session, and the phone number (orother device identifier) of the speaker. The detector may take intoaccount information about the speaker and/or the subscriber such as howoften he/she uses profanity, which, if any, profane words he/she uses,his/her emotional state, the degree to which his/her contacts (asdefined from calling history or a contact list) use profanity, etc. Aprofanity detector, or other components, may be provided for anyuser/party of the conversation.

Another optional component of the ASR system 520, for example, may be adomain-specific processor for application-specific needs such as addressrecognition, recognition of specific codes or account number formats, orrecognition of sets of terms such as names from a contact list orproduct names. The processor may detect domain specific orapplication-specific terms or use knowledge of the domain to correcterrors, format terms in a transcription, or configure a language model511 for speech recognition. In these and other embodiments, the rescorer512 may be configured to recognize domain-specific terms. Domain- orapplication-specific processing may alternatively be performed byincorporating a domain-specific grammar into the language model.

Additional components may also be added in addition to merelyrecognizing the words, including performing natural language processingto determine intent (i.e., a classification of what the person said orwants), providing a text summary of the communication session on adisplay, generating a report that tabulates key information from acommunication session such as drug dosages and appointment time andlocation, running a dialog that formulates the content and wording of averbal or text response, and text-to-speech synthesis or audio playbackto play an audio prompt or other information to one or more of theparties on the communication session.

Communication session content may also be transmitted to a digitalvirtual assistant that may use communication session content to makecalendar entries, set reminders, make purchases, request entertainmentsuch as playing music, make reservations, submit customer supportrequests, retrieve information relevant to the communication session,answer questions, send notices or invites to third parties, initiatecommunication sessions, send email or other text messages, provide inputto or display information from advertisement services, engage in socialconversations, report on news, weather, and sports, answer questions, orto provide other services typical of a digital virtual assistant. Inthese and other embodiments, the captioning service may interconnect toone or more commercial digital virtual assistants, such as via an API,to provide methods for the user to use their device to communicate withthe digital virtual assistant. The digital virtual assistant may provideresults to the user via voice, a display, sending the information toanother device such as a smartphone or to an information service such asemail, etc. For example, the user device may display the date and timeduring and/or between communication sessions.

Referring now jointly to FIGS. 6-8, these figures depict methods 600,700, and 800, each configured to transcribe audio, according to someembodiments in this disclosure. The methods illustrate how audio may betranscribed utilizing multiple ASR systems through sharing of resourcesbetween ASR systems. Alternatively or additionally, the methodsillustrate how different steps in the transcription process may beperformed by multiple ASR systems. While utilizing multiple ASR systemsto generate a transcription of audio may provide advantages of increasedaccuracy, estimation, etc., multiple ASR systems may also increasehardware and power resource utilization. An alternative that may reducehardware and power requirements is to share certain resources acrossmultiple ASR systems.

Examples in FIGS. 6-8 illustrate sharing resources across two ASRsystems, though concepts described in methods 600, 700, 800 may also beused for three or more ASR systems. In the below described embodiments,which refer to processing audio when a single device shares the outputwith multiple ASR systems, the single device may be implemented in anASR system, a server, on a device participating in the communicationsession, or one of the multiple ASR systems, among others. A moredetailed explanation of the steps illustrated in FIGS. 6-8 may bedescribed with respect to FIG. 5.

The method 600 depicts an embodiment of shared feature extraction acrossmultiple ASR systems. The method 600 may be arranged in accordance withat least one embodiment described in the present disclosure. The method600 may be performed, in some embodiments, by a device or system, suchas a transcription unit or multiple ASR systems, or another device. Inthese and other embodiments, the method 600 may be performed based onthe execution of instructions stored on one or more non-transitorycomputer-readable media. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, combined intofewer blocks, or eliminated, depending on the desired implementation.

The method may begin at block 602, wherein features of audio areextracted. The features may be extracted by a single device or ASRsystem. The features may be shared with multiple ASR systems, includingASR systems ASR1 and ASR2. Each of the ASR systems ASR1 and ASR2 mayobtain the extracted features and perform blocks to transcribe audio. Insome embodiments, ASR system ASR1 may perform blocks 604 a, 606 a, 608a, 610 a, 612 a, 614 a, and 616 a. In some embodiments, ASR system ASR2may perform blocks 604 b, 606 b, 608 b, 610 b, 612 b, 614 b, and 616 b.

At blocks 604 a and 604 b, the extracted features may be transformedinto new vectors of features. At blocks 606 a and 606 b, probabilitiessuch as phoneme probabilities may be computed. At blocks 608 a and 608b, the probabilities may be decoded into one or more hypothesissequences of words or other symbols for generating a transcription.

At blocks 610 a and 610 b, the decoded hypothesis sequence of words orother symbols may be rescored. At blocks 612 a and 612 b, capitalizationand punctuation may be determined for the rescored hypothesis sequenceof words or multiple rescored hypothesis sequence of words. At blocks614 a and 614 b, the rescored hypothesis sequence of words or multiplerescored hypothesis sequences of words may be scored. The score mayinclude an indication of a confidence that the rescored hypothesissequence of words or multiple rescored hypothesis sequences of words arethe correct transcription of the audio.

At blocks 616 a and 616 b, the rescored hypothesis sequence of words ormultiple rescored hypothesis sequences of words may be output. Althoughblocks 604 a, 606 a, 608 a, 610 a, 612 a, 614 a, and 616 a and blocks604 b, 606 b, 608 b, 610 b, 612 b, 614 b, and 616 b are describedtogether, the blocks may each be performed separately by the ASR systemsASR1 and ASR2.

The method 700 depicts an embodiment of shared feature extraction,feature transform, and phoneme calculations across multiple ASR systems.The method 700 may be arranged in accordance with at least oneembodiment described in the present disclosure. The method 700 may beperformed, in some embodiments, by a device or system, such as atranscription unit or multiple ASR systems, or another device. In theseand other embodiments, the method 700 may be performed based on theexecution of instructions stored on one or more non-transitorycomputer-readable media. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, combined intofewer blocks, or eliminated, depending on the desired implementation.

The method may begin at block 702, wherein features of audio areextracted. The features may be extracted by a single device or ASRsystem. At block 704, the extracted features may be transformed into newvectors of features. At block 706, probabilities such as phonemeprobabilities may be computed. Blocks 702, 704, and 706 may be performedby a single device or ASR system. The probabilities may be shared withmultiple ASR systems, including ASR systems ASR1 and ASR2. Each of theASR systems ASR1 and ASR2 may obtain the probabilities. In someembodiments, ASR system ASR1 may perform blocks 704 a, 706 a 708 a, 710a, 712 a, 714 a, and 716 a. In some embodiments, ASR system ASR2 mayperform blocks 708 b, 710 b, 712 b, 714 b, and 716 b.

At blocks 708 a and 708 b, the probabilities may be decoded into one ormore hypothesis sequences of words or other symbols for generating atranscription.

At blocks 710 a and 710 b, the decoded hypothesis sequence of words orother symbols may be rescored. At blocks 712 a and 712 b, capitalizationand punctuation may be determined for the rescored hypothesis sequenceof words or multiple rescored hypothesis sequences of words. At blocks714 a and 714 b, the rescored hypothesis sequence of words or multiplerescored hypothesis sequences of words may be scored. The score mayinclude an indication of a confidence that the rescored hypothesissequence of words or multiple rescored hypothesis sequences of words arethe correct transcription of the audio.

At blocks 716 a and 716 b, the rescored hypothesis sequence of words ormultiple rescored hypothesis sequences of words may be output. Althoughblocks 708 a, 710 a, 712 a, 714 a, and 716 a and blocks 708 b, 710 b,712 b, 714 b, and 716 b are described together, the blocks may each beperformed separately by the ASR systems ASR1 and ASR2.

The method 800 depicts an embodiment of shared feature extraction,feature transform, phoneme calculations, and decoding, across multipleASR systems. The method 800 may be arranged in accordance with at leastone embodiment described in the present disclosure. The method 800 maybe performed, in some embodiments, by a device or system, such as atranscription unit or multiple ASR systems, or another device. In theseand other embodiments, the method 800 may be performed based on theexecution of instructions stored on one or more non-transitorycomputer-readable media. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, combined intofewer blocks, or eliminated, depending on the desired implementation.

The method may begin at block 802, wherein features of audio areextracted. At block 804, the extracted features may be transformed intonew vectors of features. At block 806, probabilities may be computed. Atblock 808, the probabilities may be decoded into one or more hypothesissequences of words or other symbols for generating a transcription.

The blocks 802, 804, 806, and 808 may be extracted by a single device orASR system. The one or more hypothesis sequences of words or othersymbols may be shared with multiple ASR systems, including ASR systemsASR1 and ASR2. Each of the ASR systems ASR1 and ASR2 may obtain the oneor more hypothesis sequences of words or other symbols and performblocks to transcribe audio. In these and other embodiments, one or morehypothesis sequences of words may include a single hypothesis, a WCN, alattice, or an n-best list. In these and other embodiments, the n-bestlist may include a list where each item in the list is a string of wordsand may be rescored by an RNNLM or other language model. Additionally oralternatively, the one or more hypothesis sequences of words may be in aWCN or lattice, which may be rescored by an RNNLM or other languagemodel.

In some embodiments, ASR system ASR1 may perform blocks 810 a, 812 a,814 a, and 816 a. In some embodiments, ASR system ASR2 may performblocks 810 b, 812 b, 814 b, and 816 b.

At blocks 810 a and 810 b, the decoded hypothesis sequence of words orother symbols may be rescored. At blocks 812 a and 812 b, capitalizationand punctuation may be determined for the rescored hypothesis sequenceof words or multiple rescored hypothesis sequences of words. At blocks814 a and 814 b, the rescored hypothesis sequence of words or multiplerescored hypothesis sequences of words may be scored. The score mayinclude an indication of a confidence that the rescored hypothesissequence of words or multiple rescored hypothesis sequences of words arethe correct transcription of the audio.

At blocks 816 a and 816 b, the rescored hypothesis sequence of words ormultiple rescored hypothesis sequences of words may be output. Althoughblocks 804 a, 806 a, 808 a, 810 a, 812 a, 814 a, and 816 a and blocks804 b, 806 b, 808 b, 810 b, 812 b, 814 b, and 816 b are describedtogether, the blocks may each be performed separately by the ASR systemsASR1 and ASR2.

In some embodiments of methods 600, 700, and 800, the ASR system ASR2may assist the ASR system ASR1 by providing a grammar to the ASR systemASR1. In some embodiments, a grammar may be shared whether or not theASR systems share resources and whether or not they have a common audiosource. For example, in some embodiments, both ASR systems may share acommon audio source and share grammar. In some embodiments, each ASRsystem may have its own audio source and feature extraction, andgrammars may still be shared. For example (see FIG. 42), a first ASRsystem may process communication session audio and send a grammar orlanguage model to a second ASR system that may process a revoicing ofthe communication session audio. Alternatively or additionally, a firstASR system may process a revoicing of the communication session audioand send a grammar or language model to a second ASR system that mayprocess communication session audio.

In some embodiments, as depicted in methods 600, 700, and 800, ASRsystem ASR1 may use the grammar from ASR system ASR2. ASR system ASR1may use the grammar to guide a speech recognition search or inrescoring. In these and other embodiments, the decoding performed by theASR system ASR2 may use a relatively large statistical language modeland the ASR system ASR1 may use the grammar received from ASR systemASR2 120 as a language model. In these and other embodiments, thegrammar may include a structure generated by ASR system ASR2 in theprocess of transcribing audio.

In some embodiments, the grammar may be derived from a structure such asa text transcription or a rich output format such as an n-best list, aWCN, or a lattice. The grammar may be generated using output from thedecoding performed by ASR system ASR2, as illustrated in method 600 orfrom the rescoring performed by ASR system ASR2 as illustrated in method700 or method 800. The grammar may be provided, for example, to theblocks performing decoding or rescoring. The methods 600, 700, and 800are illustrative of some combinations of sharing resources. Othercombinations of resources may be similarly shared between ASR systems.For example, FIG. 40 illustrates another example of resource sharingbetween ASR systems where feature extraction is separate, and theremaining steps/components are shared among the ASR systems.

FIG. 9 is a schematic block diagram illustrating an exampletranscription unit 914, in accordance with some embodiments of thepresent disclosure. The transcription unit 914 may be a revoicedtranscription unit and may include a CA client 922 and an ASR system920. The CA client 922 may include a CA profile 908 and a text editor926.

The transcription unit 914 may be configured to receive audio from acommunication session. The transcription unit 914 may also receive otheraccompanying information such as a VAD (voice activity detection)signal, one or more phone numbers or device identifiers, a video signal,information about the speakers (such as an indicator of whether eachparty in the communication session is speaking), speaker-dependent ASRmodels associated with the parties of the communication sessiongenerating the audio received, or other meta-information. Generally,where audio is provided to an ASR system or transcription unitadditional information may also be included. The additional informationmay be included when not explicitly illustrated or described.Alternatively or additionally, communication session audio may includespeech from one or more speakers participating in the communicationsession from other locations or using other communication devices suchas on a conference communication session or an agent-assistedcommunication session.

In some embodiments, the audio may be received by the CA client 922. TheCA client 922 may broadcast the audio to a CA and capture speech of theCA as the CA revoices the words of the audio to generate revoiced audio.The revoiced audio may be provided to the ASR system 920. As describedpreviously, the CA may also use an editing interface to the text editor926 to make corrections to the transcription generated by the ASR system920 (see, for example, FIG. 1). In some embodiments, the ASR system 920may be speaker-independent such that it includes models that are trainedon multiple communication session audio and/or CA voices. Alternativelyor additionally, the ASR system 920 may be a speaker-dependent ASRsystem that is trained on the CA's voice. The models trained on the CA'svoice may be stored in the CA profile 908 that is specific for the CA.The CA profile 908 may be saved to and distributed from a profilemanager 910 so that the CA may use any of multiple CA workstations thatinclude a display, speaker, microphone, and input/output devices toallow the CA to interact with the CA client 922. In some embodiments,when the CA logs into a workstation, the CA client 922 on thatworkstation may be configured to download the CA profile 908 and providethe CA profile to the ASR system 920 to assist the ASR system 920 totranscribe the revoiced audio generated by the CA client 922 withassistance by the CA.

In some embodiments, the CA profile 908 may change the behavior of theASR system for a given CA and may include information specific to theCA. For example, the CA profile 908 may include models such as anacoustic model and language models specific to the CA. Alternatively oradditionally, the CA profile 908 may include a lexicon including wordsthat the CA has edited. The CA profile 908 may further include key wordsdefined by the CA to execute macros, to insert quick words (describedbelow with reference to FIG. 57), and as aliases to represent specificwords.

In some embodiments, the ASR system models included in the CA profile908 may be trained on communication session data, such as communicationsession audio and transcriptions from the transcription unit 914 andstored in a secure location. The training of the models on thecommunication session data may be performed by the CA client 922 or by aseparate server or device. In some embodiments, the training of themodels may occur on a particular schedule, when system resources areavailable, such as at night or when traffic is otherwise light, orperiodically, among other schedules. Additionally or alternatively,communication session data as it is captured may be transformed into ananonymous, nonreversible form such as n-grams or speech features, whichmay be further described with respect to FIG. 66. The converted form maybe used to train the ASR system models of the CA profile 908 withrespect to the CA's voice.

In some embodiments, the ASR system models in the CA profile 908 may betrained on-the-fly. Training on-the-fly may indicate that the ASR systemmodels are trained on a data sample (e.g., audio and/or text) as it iscaptured. In some embodiments, the data sample may deleted after it isused for training. In some embodiments, the data sample may be deletedbefore a processor performing training using a first batch of samplesincluding the data sample begins training using a second batch ofsamples including other data samples not in the first batch. In someembodiments, the data sample may be deleted at or near the end of thecommunication session in which the data sample is captured. These andother embodiments may be discussed in greater detail below withreference to FIG. 78. The on-fly-training may be performed by the CAclient 922 or on a separate server. Where training happens on the CAclient 922, the training process may run on one or more processors orcompute cores separate from the one or more processors or compute coresmiming the ASR system 920 or may run when CA client 922 is not engagedin providing revoiced audio to the ASR system 920 for transcriptiongeneration.

Modifications, additions, or omissions may be made to the transcriptionunit 914 and/or the components operating in transcription unit 914without departing from the scope of the present disclosure. For example,in some embodiments, the transcription unit 914 may include additionalelements, such as another ASR system and fusers among other elements.Alternatively or additionally, in some embodiments, the ASR system 920may pause processing when no voice is detected in the audio, such aswhen the audio includes silence.

FIG. 10 is a schematic block diagram illustrating another exampletranscription unit 1014, arranged accordingly to some embodiments of thepresent disclosure. The transcription unit 1014 includes an ASR system1020 and various ASR models 1006 that may be used by the ASR system 1020to generate transcriptions. The transcription unit 1014 may beconfigured to convert communication session audio, such as voice samplesfrom a conversation participant, into a text transcription for use incaptioning a communication session. Modifications, additions, oromissions may be made to the transcription unit 1014 and/or thecomponents operating in transcription unit 1014 without departing fromthe scope of the present disclosure. For example, in some embodiments,the transcription unit 1014 may include additional elements, such asother ASR systems and fusers among other elements.

FIG. 11 is a schematic block diagram illustrating another exampletranscription unit 1114, in accordance with some embodiments of thepresent disclosure. In some embodiments, the transcription unit 1114 maybe configured to identity a person from which speech is included inaudio received by the transcription unit 1114. The transcription unit1114 may also be configured to train at least one ASR system, forexample, by training or updating models, using samples of the person'svoice. In these and other embodiments, the ASR system may bespeaker-dependent or speaker-independent. Examples of models that may betrained may include acoustic models, language models, lexicons, andruntime parameters or settings, among other models, including modelsdescribed with respect to FIG. 5.

The transcription unit 1114 may include an ASR system 1120, a diarizer1102, a voiceprints database 1104, an ASR model trainer 1122, and aspeaker profile database 1106. In some embodiments, the diarizer 1102may be configured to identify a device that generates audio for which atranscription is to be generated by the transcription unit 1114. In someembodiments, the device may be a communication device connected to thecommunication session.

In some embodiments, the diarizer 1102 may be configured to identify adevice using a phone number or other device identifier. In these andother embodiments, the diarizer 1102 may distinguish audio thatoriginates from the device from other audio in a communication sessionbased on from which line the audio is received. For example, in a stereocommunication path, the audio of the device may appear on a first lineand the audio of another device may appear on a second line. As anotherexample, on a conference communication session, the diarizer 1102 mayuse a message generated by the bridge of the conference communicationsession that may indicate which line carries audio from the separatedevices participating in the conference communication session.

In some embodiments, the diarizer 1102 may be configured to determine iffirst audio from a first device and at least a portion of second audiofrom a second device appear on a first line from the first device. Inthese and other embodiments, the diarizer 1102 may be configured to usean adaptive filter to convert the second audio signal from the seconddevice to a filtered form that matches the portion of the second audiosignal appearing on the first line so that the filtered form may besubtracted from the first line to thereby remove the second audio signalfrom the first line. Alternatively or additionally, the diarizer 1102may utilize other methods to separate first and second audio signals ona single line or eliminate signal leak or crosstalk between audiosignals. The other methods may include echo cancellers and echosuppressors, among others.

In some embodiments, people using an identified device may be consideredto be a single speaker group and may be treated by the diarizer 1102 asa single person. Alternatively or additionally, the diarizer 1102 mayuse speaker identification to identify the voices of various people thatmay use a device for communication sessions or that may use devices toestablish communication sessions from a communication service, such as aPOTS number, voice-over-internet protocol (VOIP) number, mobile phonenumber, or other communication service. In these and other embodiments,the speaker identification employed by the diarizer 1102 may includeusing voiceprints to distinguish between voices. In these and otherembodiments, the diarizer 1102 may be configured to create a set ofvoiceprints for speakers using a device. The creation of voiceprintmodels will be described in greater detail below with reference to FIG.62.

In some embodiments, to select between people using the voiceprints, thediarizer 1102 may collect a voice sample from audio originating at adevice. The diarizer 1102 may compare collected voice samples toexisting voiceprints associated with the device. In response to thevoice sample matching a voiceprint, the diarizer 1102 may designate theaudio as originating from a person that is associated with the matchingvoiceprint. In these and other embodiments, the diarizer 1102 may alsobe configured to use the voice sample of the speaker to update thevoiceprint so that the voice match will be more accurate in subsequentmatches. In response to the voice sample not matching a voiceprint, thediarizer 1102 may create a new voiceprint for the newly identifiedperson.

In some embodiments, the diarizer 1102 may maintain speaker profiles ina speaker profile database 1106. In these and other embodiments, eachspeaker profile may correspond to a voiceprint in the voiceprintdatabase 1104. In these and other embodiments, in response to the voicesample matching a voiceprint the diarizer 1102 may be configured toaccess a speaker profile corresponding to the matching voiceprint.

In some embodiments, the speaker profile may include ASR models or linksto ASR models such as acoustic models, feature transformation modelssuch as MLLR or fMLLR transforms, language models, vocabularies,lexicons, and confidence models, among others. The ASR models associatedwith the speaker profile may be models that are trained based on thevoice profile of the person associated with the speaker profile. Inthese and other embodiments, the diarizer 1102 may make the ASR modelsavailable to the ASR system 1120 which may use the ASR models to performspeech recognition for speech in audio from the person. When using theASR models associated with a speaker profile, the ASR system 1120 may beconfigured as a speaker-dependent system with respect to the personassociated with the speaker profile.

In response to the voice sample not matching a voiceprint, the diarizer1102 may be configured to instruct the model trainer 522 to train ASRmodels for the identified voice using the voice sample. The diarizer1102 may also be configured to save/update profiles, including adaptedASR models, to the profile associated with the matching voiceprint. Insome embodiments, the diarizer 1102 may be configured to transmitspeaker information to the device upon matching a voiceprint in thevoiceprint database 1104.

An example of the operation of the transcription unit 1114 is nowprovided. Audio of a communication session between two devices may bereceived by the transcription unit 1114. The communication session maybe between a first device of a first user (e.g., the subscriber to thetranscription service) and a second device of a second user, the speechof which may be transcribed. The diarizer 1102 may transmit an indicatorsuch as “(new caller)” or “(speaker 1)” to the first device forpresentation by the first device. In response to the diarizer 1102detecting a voice change in the audio being received from the seconddevice (i.e., the voice switches from a previous voice to a new voice),the diarizer 1102 may transmit an indicator such as “(new caller)” or“(speaker 2)” to the first device for presentation. The diarizer 1102may compare the new voice to voiceprints from the voiceprint database1104 associated with the second device when the second device is knownor not new.

In response to the diarizer 1102 identifying or matching the new voiceto an existing voiceprint (including voiceprints from previouscommunication sessions), an indicator identifying the matched speakermay be transmitted to the first device and ASR models trained for thenew voice may be provided to an ASR system generating transcriptions ofaudio that includes the new voice. In response to the diarizer 1102 notmatching the new voice, the diarizer 1102 may send an indication to thefirst device that the person is new or unidentified, and the diarizer1102 may train a new speaker profile, model, and voiceprint for the newperson.

Modifications, additions, or omissions may be made to the transcriptionunit 1114 and/or the components operating in transcription unit 1114without departing from the scope of the present disclosure. For example,in some embodiments, the transcription unit 1114 may include additionalelements, such as other ASR systems, a CA client, and fusers among otherelements.

As another example, the speaker profile database 1106, the voiceprintdatabase 1104, the ASR model trainer 1122, and the diarizer 1102 areillustrated in FIG. 11 as part of the transcription unit 1114, but thecomponents may be implemented on other systems located locally or atremote locations and on other devices.

FIG. 12 is a schematic block diagram illustrating multiple transcriptionunits in accordance with some embodiments of the present disclosure. Themultiple transcription units may include a first transcription unit 1214a, a second transcription unit 1214 b, and a third transcription unit1214 c. The transcription units 1214 a, 1214 b, and 1214 c may bereferred to collectively as the transcription units 1214.

In some embodiments, the first transcription unit 114 a may include anASR system 1220 and a CA client 1222. The ASR system 1220 may be arevoiced ASR system that includes speaker-dependent models provided bythe CA client 1222. The ASR system 1220 may operate in a manneranalogous to other ASR systems described in this disclosure. The CAclient 1222 may include a CA profile 1224 and may be configured tooperate in a manner analogous to other CA clients described in thisdisclosure.

In some embodiments, the CA profile 1224 may include models such as alexicon (a.k.a. vocabulary or dictionary), an acoustic model (AM), alanguage model (LM), a capitalization model, and a pronunciation model.The lexicon may contain a list of terms that the ASR system 1220 mayrecognize and may be constructed from the combination of severalelements including an initial lexicon and terms added to the lexicon bythe CA client 1222 as directed by a CA associated with the CA client1222. In these and other embodiments, a term may be letters, numbers,initials, abbreviations, a word, or a series of words.

In some embodiments, the CA client 1222 may add terms to a lexiconassociated with the CA client 1222 in several ways. The ways in which aterm may be added may include: adding an entry to the lexicon based oninput from a CA, adding a term to a list of problem terms ordifficult-to-recognize terms for training by a module used by the ASRsystem 1220, and obtaining a term from the text editor based on the termbeing applied as an edit or correction of a transcription. In someembodiments, in addition to the term being added to the lexicon, anindication of how the term is to be pronounced may also be added to thelexicon.

In some embodiments, terms added to the lexicon of the CA profile 1224may be used for recognition by the ASR system 1220. Additionally oralternatively, terms added to the lexicon of the CA profile 1224 mayalso be added to a candidate lexicon database 1208. A candidate lexicondatabase 1208 may include a database of terms that may be considered fordistribution to other CA clients in a transcription system that includesthe transcription units 1214 or other transcription systems.

In some embodiments, a language manager tool 1210 may be configured tomanage the candidate lexicon database 1208. For example, in someembodiments, the language manager tool 1210 may manage the candidatelexicon database 1208 automatically or based on user input. Managementof the candidate lexicon database 1208 may include reviewing the termsin the candidate lexicon database 1208. Once a candidate term has beenreviewed, the candidate lexicon database 1208 may be updated to eitherremove the term or mark the term as accepted or rejected. A term markedas accepted may be provided to a global lexicon database 1212. Theglobal lexicon database 1212 may provide lexicons to CA clients ofmultiple transcription units 1214 among other CA clients in atranscription system. The global lexicon database 1212 may bedistributed to CA clients so that the terms recently added to the globallexicon database 1212 may be provided to the ASR systems associated withthe CA clients such that the ASR systems may be more likely to recognizeand generate a transcription with the terms.

In some embodiments, the language manager tool 1210 may determine toaccept or reject terms in the candidate lexicon database 1208 based oncounts associated with the terms. Alternatively or additionally, thelanguage manager tool 1210 may evaluate whether a term should bereviewed based on a count associated with a term.

In some embodiments, for a particular term, counts of the term mayinclude: (1) the number of different CA clients that have submitted theterm to the candidate lexicon database 1208; (2) the number of times theterm has been submitted to the candidate lexicon database 1208, by a CAclient, by a group of CA clients, or across all CA clients; (3) thenumber of times the term appears at the output of an ASR system; (4) thenumber of times the term is provided to be displayed by a CA client forcorrection by a CA; (5) the number of times a text editor receives theterm as a correction or edit; (6) the number of times a term has beencounted in a particular period of time, such as the past m days, where mis, for example 3, 7, 14, or 30; and (7) the number of days since theterm first appeared or since the particular count of the term, such asthe 100; 500; 1,000; among other amounts. In some embodiments, more thanone type of count as described above may be considered. For example, acombination of two, three, or four of the different types of counts maybe considered. In these and other embodiments, for combinations ofcounts, the different counts in a combination may be normalized andcombined to allow for comparison. In these and other embodiments, theone or more of the different type of counts may be weighted.

In some embodiments, the language manager tool 1210 may evaluate whethera term should be reviewed and/or added/rejected based on a countassociated with the term and other information. The other informationmay include: Internet searches, including news broadcasts, lists ofnames, word corpora, and queries into dictionaries; and evidence thatthe term is likely to appear in conversations in the future based on theterm appearing in titles of new movies, slang dictionaries, or the termbeing a proper noun, such as a name of city, place, person, company, orproduct.

An example of handling a term is now provided. In this example, the termmay be “skizze,” which may be a previously unknown word. One hundred CAclients may add the term “skizze,” to their CA profile or to thecandidate lexicon database 1208. Additionally, the term may appear intranscriptions seven-hundred times over thirty days. The languagemanager tool 1210, based on these counts meeting selected criteria, mayautomatically add the term to the global lexicon database 1212.Additionally or alternatively, the language manager tool 1210 maypresent the term, along with its counts and other usage statistics, to alanguage manager (a human administrator) via a user interface wherecandidate terms are presented in a list. The list may be sorted bycounts. In these and other embodiments, the language manager tool 1210may accept inputs from the language manager regarding how to handle apresented term.

In some embodiments, the global lexicon database 1212, after beingprovided to the CA client 1222, may be used by the CA client 1222 invarious ways. For example, the CA client 1222 may use the terms in theglobal lexicon database 1212 in the following ways: (1) if the CA client1222 obtains a term from a CA through a text editor that is not part ofthe base lexicon, the lexicon of the CA client 1222 particular to theCA, the global lexicon database 1212, or other lexicons used by thetranscription system such as commercial dictionaries, the CA client 1222may present a warning, such as a pop-up message, that the term may beinvalid. In these and other embodiments, when a warning is presented,the term may not be able to be entered. Alternatively or additionally,when a warning is presented, the term may be entered based on inputobtained from a CA. Alternatively or additionally, when a warning ispresented, the CA client 1222 may provide an alternative term from alexicon; (2) terms in the global lexicon database 1212 may be includedin the ASR system vocabulary so that the term can be recognized or moreeasily recognized; and (3) terms that are missing from the globallexicon database 1212 or, alternatively, terms that have been rejectedby the language manager or language manager tool 1210, may be removedfrom the CA client 1222.

In some embodiments, the CA client 1222 may use multiple lexicons. Forexample, the ASR system 1220 may use a first lexicon or combination oflexicons for speech recognition and a text editor of the CA client 1222may use a second lexicon or set of lexicons as part of or in conjunctionwith a spell checker.

Modifications, additions, or omissions may be made to the transcriptionunits 1214 and/or the components operating in transcription units 1214without departing from the scope of the present disclosure. For example,in some embodiments, there may be more or less than three transcriptionunits 1214 that may use the global lexicon database 1212. The threetranscription units 1214 are merely illustrative. Alternatively oradditionally, the first transcription unit 1214 a may include additionalelements, such as other ASR systems and fusers among other elements.

FIGS. 13-17, among others, describe various systems and methods that maybe used to merge two or more transcriptions generated by separate ASRsystems to create a fused transcription. In some embodiments, the fusedtranscription may include an accuracy that is improved with respect tothe accuracy of the individual transcriptions combined to generate thefused transcription.

FIG. 13 is a schematic block diagram illustrating combining the outputof multiple ASR systems in accordance with some embodiments of thepresent disclosure. FIG. 13 may include a first ASR system 1320 a, asecond ASR system 1320 b, a third ASR system 1320 c, and a fourth ASRsystem 1320 d, collectively or individually referred to as the ASRsystems 1320.

In some embodiments, the ASR systems 1320 may be speaker-independent,speaker-dependent, or some combination thereof. Alternatively oradditionally, each of ASR systems 1320 may include a differentconfiguration, the same configuration, or some of the ASR systems 1320may have a different configuration than other of the ASR systems 1320.The configurations of the ASR systems 1320 may be based on ASR modulesthat may be used by the ASR systems 1320 to generate transcriptions. Forexample, in FIG. 13, the ASR system 1320 may include a lexicon modulefrom a global lexicon database 1312. Alternatively or additionally, theASR systems 1320 may each include different lexicon modules.

In some embodiments, the audio provided to the ASR systems 1320 may berevoiced, regular, or a combination of revoiced and regular.Alternatively or additionally, the ASR systems 1320 may be included in asingle transcription unit or spread across multiple transcription units.Additionally or alternatively, the ASR systems 1320 may be part ofdifferent API services, such as services provided by different vendors.

In some embodiments, each of the ASR systems 1320 may be configured togenerate a transcription based on the audio received by the ASR systems1320. The transcriptions, referred to sometimes in this and otherembodiments as “hypotheses,” may have varying degrees of accuracydepending on the particular configuration of the ASR systems 1320. Insome embodiments, the hypotheses may be represented as a string oftokens. The string of tokens may include one or more of sentences,phrases, or words. A token may include a word, subword, character, orsymbol.

FIG. 13 also illustrates a fuser 1324. In some embodiments, the fuser1324 may be configured to merge the transcriptions generated by the ASRsystems 1320 to create a fused transcription. In some embodiments, thefused transcription may include an accuracy that is improved withrespect to the accuracy of the individual transcriptions combined togenerate the fused transcription. Additionally or alternatively, thefuser 1324 may generate multiple transcriptions.

Examples of different configurations of the ASR systems 1320 (using twoASR systems, ASR1 and ASR2 as examples), the respective outputtranscriptions of which may be combined through fusion, are describedbelow in Table 3.

TABLE 3 1. ASR1 and ASR2 may be built or trained by different vendorsfor different applications. 2. ASR1 and ASR2 may be configured ortrained differently or use different models. 3. ASR2 may run in areduced mode or may be “crippled“ or deliberately configured to deliverresults with reduced accuracy, compared to ASR1. Because ASR2 may tendto perform reasonably well with speech that is easy to understand, andtherefore closely match the results of ASR1. the agreement rate betweenASR1 and ASR2 may be used as a measure of how difficult it is torecognize the speech. The rate may therefore be used to predict theaccuracy of ASR1. ASR2, and/or other ASR systems. Examples of crippledASR system configurations may include: a. ASR2 may use a different orsmaller language model, such as a language model containing fewer n-gram probabilities or a neural net with fewer nodes or connections. Ifthe ASR1 LM is based on n- grants, the ASR2 LM may be based on unigramsor n-grams where n for ASR2 is smaller than n for ASR1. b. ASR2 may addnoise to or otherwise distort the input audio signal. c. ASR2 may use acopy of the input signal that is shifted in time, may have speechanalysis frame boundaries starting at different times from those ofASR1, or may operate at a frame rate different from ASR1. As a result,speech samples may be divided into frames differently, compared to ASR1,and frame-based signal analysis may result in a set of extractedfeatures different from those of ASR1. d. ASR2 may use an inferioracoustic model, such as one using a smaller DNN. e. ASR2 may use arecognizer trained on less data or on training data that is mismatchedto the production data. f. ASR2 may be an old version of ASR1. Forexample, it may be trained on older data or it may lack certainimprovements. g· ASR2 may perform a beam search using a narrower beam,relative to the beam width of ASR1. h. ASR1 and/or ASR2 may combine theresults from an acoustic model and a language model to obtain one ormore hypotheses, where the acoustic and language models are assignedrelatively different weights. ASR2 may use a different weighting for theacoustic model vs. the language model, relative to the weighting used byASR1. i. Except for the differences deliberately imposed to make ASR2inferior, ASR2 may be substantially identical to ASR1, in that it mayuse substantially identical software modules, hardware, trainingprocesses, configuration parameters, and training data. 4. ASR1 and ASR2may use models that are trained on different sets of acoustic and/ortext data (see Table 4).

Alternatively or additionally, examples of different configurations ofthe ASR systems 1320 may include the ASR systems 1320 being built usingdifferent software, trained on different data sets, configured withdifferent runtime parameters, and provided audio that has been alteredin different ways, or otherwise configured to provide different results.In these and other embodiments, the data sets may include the data thatmay be used to train modules that are used by the ASR systems 1320. Inthese and other embodiments, the different data sets may be divided intomultiple training sets using one or more of several methods as listedbelow in Table 4. Additional details regarding dividing training setsare provided with respect to FIG. 77 among others.

TABLE 4 1. Divide the data based on calling patterns such as longcommunication sessions vs. short communication sessions, communicationsessions to numbers frequently called (such as friends) vs.communication sessions to numbers infrequently called (such asstrangers), or inbound communication sessions vs. outbound communicationsessions. 2. Cluster the data into groups, for example by trainingseveral recognizers, associating each data point with the recognizerthat gives the data point the highest ASR confidence score, retrainingeach recognizer using the data points associated with the recognizer,and iterating to form clusters. 3. Divide the data by conversationaltopic. 4. Divide the data by the service used to collect the data.Examples of services include a transcribed communication session,communication session transcription and analytics, voice mailtranscription, personal voice assistants, IVR services, reservationservices, etc. 5. Divide the data by time, such as a range of dates ortime of day. 6. Divide the data by account type (see Table 10). 7.Divide the data by speaker category or demographic such as accent ordialect, geographical region, gender, age (child, elderly, etc.), speechimpaired, hearing impaired, etc. 8. Separate audio spoken by a set offirst user(s) from audio spoken by a set of second user(s). 9. Separaterevoiced audio from regular audio. 10. Separate data from phonesconfigured to present transcriptions from data from other phones.

Combining of transcriptions to generate a fused transcription may havemultiple beneficial applications in a transcription system including:(1) helping to provide more accurate transcriptions, for example when aspeaker who is particularly difficult to understand or when accuracy ismore critical, such as with high-priority communication sessions—seeitem 76 of Table 5); (2) helping to provide more accurate transcriptionsfor training models, notably acoustic models and language models; (3)helping to provide more accurate transcriptions for evaluating CAs andmeasuring ASR performance; (4) combining results from an ASR systemusing revoiced audio and an ASR system using regular audio to helpgenerate a more accurate transcription; and (5) tuning a transcriptionunit/transcription system for better performance by adjusting thresholdssuch as confidence thresholds and revoiced/regular ASR selectionthresholds, by measuring revoiced ASR or regular ASR accuracy, and forselecting estimation, prediction, and transcription methods.

In some embodiments, the fuser 1324 may be configured to combine thetranscriptions by denormalizing the input hypotheses into tokens. Inthese and other embodiments, the tokens may be aligned, and a votingprocedure may be used to select a token for use in the outputtranscription of the fuser 1324. Additional information regarding theprocessing performed by the fuser 1324 may be provided with respect toFIG. 14.

In some embodiments, the fuser 1324 may be configured to utilize one ormore neural networks, where the neural networks process multiplehypotheses and output the fused hypothesis. In some embodiments, thefuser 1324 may be implemented as ROVER (Recognizer Output Voting ErrorReduction), a method developed by NIST (National Institute of Scienceand Technology). Modifications, additions, or omissions may be made toFIG. 13 and/or the components operating in FIG. 13 without departingfrom the scope of the present disclosure. For example, in someembodiments, a transcription from a human, such as from a stenographymachine, may be provided as an input hypothesis to the fuser 1324.

FIG. 14 illustrates a process 1400 to fuse multiple transcriptions. Theprocess 1400 may be arranged in accordance with at least one embodimentdescribed in the present disclosure. The process 1400, generally, mayinclude generating transcriptions of audio and fusing the transcriptionsof the audio. For example, the process 1400 may include a transcriptiongeneration process 1402, denormalize text process 1404, align textprocess 1406, voting process 1408, normalize text process 1409, andoutput transcription process 1410. The transcription generation process1402 may include a first transcription generation process 1402 a, asecond transcription generation process 1402 b, and a thirdtranscription generation process 1402 c. The denormalize text process1404 may include a first denormalize text process 1404 a, a seconddenormalize text process 1404 b, and a third denormalize text process1404 c.

The transcription generation process 1402 may include generatingtranscriptions from audio. The transcription generation process 1402 maybe performed by ASR systems. For example, the first transcriptiongeneration process 1402 a, the second transcription generation process1402 b, and the third transcription generation process 1402 c may beperformed by the first ASR system 1320 a, the second ASR system 1320 b,and the third ASR system 1320 c, respectively, of FIG. 13. Thetranscriptions may be generated in the manner described with respect tothe ASR systems 1320 of FIG. 13 and is not repeated here. In these andother embodiments, the transcriptions generated by the transcriptiongeneration process 1402 may each include a set of hypotheses. Eachhypothesis may include one or more tokens such as words, subwords,letters, or numbers, among other characters.

In some embodiments, the denormalize text process 1404, the align textprocess 1406, the voting process 1408, the normalize text process 1409,and the output transcription process 1410 may be performed by a fuser,such as the fuser 1324 of FIG. 13 or the fuser 124 of FIG. 1.

The first denormalize text process 1404 a, the second denormalize textprocess 1404 b, and the third denormalize text process 1404 c may beconfigured to receive the tokens from the first transcription generationprocess 1402 a, the second transcription generation process 1402 b, andthe third transcription generation process 1402 c, respectively. Thedenormalize text process 1404 may be configured to cast the receivedtokens into a consistent format. In short, the term “denormalize” asused in this disclosure may include a process of converting tokens,e.g., text, into a less ambiguous format that may reduce the likelihoodof multiple interpretations of the tokens. For example, a denormalizeprocess may convert an address from “123 Lake Shore Dr.,” where “Dr.”may refer to drive or doctor, into “one twenty three lake shore drive.

Generally, generated transcriptions, whether from an ASR system or ahuman, may be in a form that is easily read by humans. For example, if aspeaker in a phone communication session says, “One twenty three LakeShore Drive, Chicago Ill.,” the transcription may read as “123 LakeShore Dr. Chicago Ill.” This formatting process is called normalization.While the normalization formatting process may make transcriptionseasier to read by humans, the normalization formatting process may causean automatic transcription alignment and/or voting tool to count falseerrors that arise from formatting, rather than content, even when thetranscription is performed correctly. Similarly, differences informatting may cause alignment or voting errors. Alternatively oradditionally, the normalization formatting process may not be consistentbetween different ASR systems and people. As a result, a transcriptionbased on the same audio from multiple ASR systems and a referencetranscription may be formatted differently. For these reasons,denormalizing may be useful in reducing false errors based on formattingbecause the denormalizing converts the tokens into a uniform format.Additionally or alternatively, the fuser may incorporate rules ofequivalency, such as “gonna”=“going to” or “she's”=“she is,” into thealignment and/or voting processes.

In these and other embodiments, the normalization formatting process mayalso result in inaccurate scoring of transcriptions when a referencetranscriptions in compared to a hypothesis transcription. The scoring ofthe transcriptions may relate to the determining an accuracy or errorrate of a hypothesis transcriptions as discussed later in thisdisclosure. In these and other embodiments, the reference transcriptionsand hypothesis transcriptions may be denormalized to reduce false errorsthat may result in less accurate score for hypothesis transcriptions.

During the denormalize text process 1404, the tokens may be“denormalized” such that most or all variations of a phrase may beconverted into a single, consistent format. For example, all spellingsof the name “Cathy,” including “Kathy,” “Kathie,” etc., may be convertedto a single representative form such as “Kathy” or into a tag thatrepresents the class such as “<kathy>.” Additionally or alternatively,the denormalize text process 1404 may save the normalized form of a wordor phrase before denormalization, then recall the normalized form afterdenormalization. This may be beneficial because if a CA edits a word tobe “Cathy” and an ASR system transcribes the word as “Kathie” and bothare denormalized to “Kathy” both input spellings may be lost which mayreduce potential data for future training of models. In someembodiments, the denormalize text process 1404 may be configured to saveand recall the original form of the candidate word, such as bydenormalizing the token to a list form that allows multiple options suchas “{Cathy, Kathy, Kathie}” and “Kathy” may be denormalized as “{Kathy,Cathy, Kathie},” where the first element in the list is the originalform. In these and other embodiments, the list form may be used foralignment and voting and the first element of the list (or the savedoriginal form) may be used for display. The denormalize text process1404 may provide the denormalized text/tokens to the align text process1406.

The align text process 1406 may be configured to align tokens in eachdenormalized hypothesis so that similar tokens are associated with eachother in a token group. By way of explanation and not implementation,each hypothesis may be inserted into a row of a spreadsheet or database,with matching words from each hypothesis arranged in the same column.Additionally or alternatively, the align text process 1406 may addvariable or constant delay to synchronize similar tokens. The addingvariable or constant delay may be performed to compensate fortranscription processes being performed with varied amounts of latency.

For example, if a revoiced ASR system has greater latency than anon-revoiced ASR system, the align text process 1406 may shift theoutput of the non-revoiced ASR system in time so that the non-revoicedoutput is more closely synchronized with output from the revoiced ASRsystem. The align text process 1406 may provide the aligned tokens tothe voting process 1408.

The voting process 1408 may be configured to determine an ensembleconsensus from each token group. Returning to the previous spreadsheetexample, each column of the spreadsheet may include the candidate tokensfrom the different hypothesis transcriptions. The voting process 1408may analyze all of the candidate tokens and, for example, voting may beused to select a token that appears most often in the column.

In some embodiments, such as for training models, the output of thevoting process 1408 may be used in its denormalized form. For example,if a transcription is denormalized at denormalize text process 1404(e.g., a “21” may be converted to “twenty one”), the text may remain inits denormalized form and the voting process 1408 may providedenormalized text (e.g., “twenty one”) to a model trainer.

In some embodiments, the voting process 1408 may provide an output tothe normalize text process 1409. The normalize text process 1409 may beconfigured to cast the fused output text from the voting process 1408into a more human-readable form. The normalize text process 1409 mayutilize one or more of several methods, including, but not limited to:

-   -   1. The normalized form from an input hypothesis may be saved        during the denormalize text process 1404 and the normalize text        process 1409 may recall the normalized form, as described above        for the name “Kathy,” and may reapply the normalization        formatting to the voting output. The normalized form of a given        token or set of tokens may be taken, for example, from the input        that provides the winning token or set of tokens in the voting        process.    -   2. The normalize text process 1409 may use a normalization model        that may be constructed from normalized and denormalized text        using, for example, the method described below with reference to        FIG. 58, but where the model trainer inputs are reversed        (compared to the description of FIG. 58) so that it learns to        convert a denormalized string to a normalized string.    -   3. The normalize text process 1409 may use a normalization model        that may be constructed from a corpus of normalized and        denormalized text using the method described below with        reference to FIG. 15 for training a punctuation model 1506 or        capitalization model 1518.    -   4. The normalize text process 1409 may use the method described        below with reference to FIG. 16 for removing and reinserting        punctuation and capitalization for text.

An example of the process 1400 is now provided. In this example, aspeaker may say “OK, let's meet at four.” During the transcriptiongeneration process 1402, three different ASR systems (e.g., ASR systems1320 of FIG. 13) may each generate one of the below hypotheses:

-   -   1. OK, let's meet more.    -   2. OK, says meet at 4:00.    -   3. OK, ha let's meet at far.

During the denormalize text process 1404, these hypotheses may bedenormalized to yield the following denormalized hypotheses:

-   -   1. ok let us meet more    -   2. ok says meet at four o'clock    -   3. ok ha let us meet at far

The align text process 1406 may align the tokens, e.g. the words in theabove hypotheses, so that as many identical tokens as possible lie ineach token group. In some embodiments, the alignment may reduce the editdistance (the minimum number of insertions, deletions, and substitutionsto convert one string to the other) or Levenshtein distance betweendenormalized hypotheses provided to the align text process 1406 afterthe denormalized hypotheses have been aligned. Additionally oralternatively, the alignment may reduce the edit or Levenshtein distancebetween each aligned denormalized hypothesis and the fusedtranscription. Where a hypothesis does not have a token for a giventoken group, a tag such as a series of “-” characters may be insertedinto the token group for the missing token. An example of the insertionof a tag into token groups is provided below with respect to thehypotheses from above. The token groups are represented by columns thatare separated by tabs in the below example.

1. o k — let us meet — more — 2. o k — says — meet at four o'clock 3. ok ha let us meet at far —

The voting process 1408 may be configured to examine each token groupand determine the most likely token for each given group. The mostlylikely token for each given group may be the token with the mostoccurrences in the given group. For example, the most frequent token inthe fourth token group, which includes tokens “let,” “says,” and “let,”is “let.” When multiple tokens appear the same number of times, such aswhen each hypothesis contains a different token in a given token group,any of several methods may be used to break the tie, including but notlimited to, selecting a token at random or selecting the token from theASR system determined to be most reliable. In these and otherembodiments, selecting a token from a token group may be referred to asvoting. In these and other embodiments, the token with the most votesmay be selected from its respective token group.

In some embodiments, other methods for aligning and/or voting may beused than those described above, including ROVER, alternate methods formultiple sequence alignment, BAYCOM (Bayesian Combination), CNC(confusion network combination), frame-based system combination orminimum EWER (time frame word error), and hidden Markov models used formultiple sequence alignment. Additionally or alternatively, a neuralnetwork may be used for aligning and/or voting. For example, hypothesesmay be input into a neural network, using an encoding method such asone-hot or word embedding, and the neural network may be trained togenerate a fused output. This training process may utilize referencetranscriptions as targets for the neural network output.

Additionally or alternatively, other criteria that may be used with orin addition to voting, or other methods for fusion and voting asdescribed above, may be used to select the most suitable or correcttoken from a token group. Alternatively or additionally, theseadditional criteria may also be used to break ties in a voting scheme.In some embodiments, the additional criteria may include probability,confidence, likelihood, or other statistics from models that describeword or error patterns, and other factors that weigh or modify a scorederived from word counts. For example, a token from an ASR system withrelatively higher historical accuracy may be given a higher weight.Historical accuracy may be obtained by running ASR system accuracy testsor by administering performance tests to the ASR systems. Historicalaccuracy may also be obtained by tracking estimated accuracy onproduction traffic and extracting statistics from the results.

Additional criteria may also include an ASR system including arelatively higher estimated accuracy for a segment (e.g., phrase,sentence, turn, series, or session) of words containing the token. Yetanother additional criterion might be analyzing a confidence score givento a token from the ASR system that generated the token.

Another additional criterion may be to consider tokens from an alternatehypothesis generated by an ASR system. For example, an ASR system maygenerate multiple ranked hypotheses for a segment of audio. The tokensmay be assigned weights according to each token's appearance in aparticular one of the multiple ranked hypotheses. For example, thesecond-best hypothesis from an n-best list or word position in a wordconfusion network (“WCN”) may receive a lower weight than the besthypothesis. Thus, tokens from the lower second-best hypothesis may beweighted less than tokens from the best hypothesis. In another example,a token in an alternate hypothesis may receive a weight derived from afunction of the relative likelihood of the token as compared to thelikelihood of a token in the same word order position of the besthypothesis. Likelihood may be determined by a likelihood score from anASR system that may be based on how well the hypothesized word matchesthe acoustic and language models of the ASR system.

In some embodiments, another criteria that may be considered by thevoting process 1408 when selecting a token may include the error type.In these and other embodiments, the voting process 1408 may giveprecedence to one type of error over another when selecting betweentokens. For example, when the error type is a missing token from a tokengroup, the voting process 1408 may select insertion of tokens overdeletion of tokens. A missing token from a token group may refer to thecircumstance for a particular token group when a first hypothesis doesnot include a token in the particular token group and a secondhypothesis does include a token in the particular token group. In theseand other embodiments, insertion of a token may refer to using the tokenin the particular token group in an output. Deletion of a token mayrefer to not using the token in the particular token group in theoutput. For example, if two hypotheses include tokens and token groupsas follows:

1. I like cats 2. I — catsthen the voting process 1408 may be configured to select insertion oftokens rather than deletion of tokens. In these and other embodiments,the voting process 1408 may select the first hypothesis as the correctone. Alternatively or additionally, the voting process 1408 may selectdeletion of tokens in place of insertion of tokens.

Additionally or alternatively, the voting process 1408 may selectinsertion or deletion based on the type of ASR systems that results inthe missing tokens. For example, the voting process 1408 may considerinsertions from a revoiced ASR system differently from insertions from anon-revoiced ASR system. For example, if the non-revoiced ASR systemomits a token that the revoiced ASR system included, the voting process1408 may select insertion of the token and output the result from therevoiced ASR system. In contrast, if the revoiced ASR system omits atoken that the non-revoiced ASR system included, the voting process 1408may output the non-revoiced ASR system token only if one or moreadditional criteria are met, such as if the language model confidence inthe non-revoiced ASR system word exceeds a particular threshold. Asanother example, the voting process 1408 may consider insertions from afirst ASR system miming more and/or better models than a second ASRsystem differently than insertions from the second ASR system.

In some embodiments, another criteria that may be considered by thevoting process 1408 when selecting a token may include an energy orpower level of the audio files from which the transcriptions aregenerated. For example, if a first hypothesis does not include a tokenrelative to a second hypothesis, then the voting process 1408 may takeinto account the level of energy in the audio file corresponding to thedeleted token. Various examples of selecting between tokens based onenergy levels are now provided.

In a first example, the voting process 1408 may include a bias towardsinsertion (e.g., the voting process 1408 may select the phrase “I likecats” in the above example) if an energy level in one or more of theinput audio files during the period of time corresponding to theinserted token (e.g., “like”) is higher than a high threshold. In theseand other embodiments, the voting process 1408 may include a biastowards deletion (e.g., selecting “I cats”) if the energy level in oneor more of the input audio files during the period of time correspondingto the inserted word is lower than a low threshold. The high and lowthresholds may be based on energy levels of human speech. Additionallyor alternatively, the high and low thresholds may be set to values thatincrease accuracy of the fused output. Additionally or alternatively,the high and low thresholds may both be set to a value midway betweenaverage speech energy and the average energy of background noise.Additionally or alternatively, the low threshold may be set just abovethe energy of background noise and the high threshold may be set justbelow the average energy of speech.

In a second example, the voting process 1408 may include a bias towardsinsertions if the energy level is lower than the low threshold. In athird example, the voting process 1408 may include a bias towardsnon-revoiced ASR system insertions when the energy level from therevoiced ASR system is low. In these and other embodiments, thenon-revoiced ASR system output may be used when the energy level in therevoiced ASR system is relatively low. A relatively low energy level ofthe audio used by the revoiced ASR system may be caused by a CA notspeaking even when there are words in the regular audio to be revoiced.In these and other embodiments, the energy level in the non-revoiced ASRsystem may be compared to the energy level in the revoiced ASR system.When there is a difference between the energy levels that is greaterthan a difference threshold, the non-revoiced ASR system output may beused. In these and other embodiments, the difference threshold may bebased on the energy levels that occur when a CA is not speaking, whenthere are words in the audio or the CA is speaking only a portion of thewords in the audio. As a result, the revoiced audio may not includewords that the regular audio includes thereby resulting in a differencein the energy levels of the audio processed by the revoiced ASR systemand the non-revoiced ASR system.

In some embodiments, another criteria that may be considered by thevoting process 1408 when selecting a token may include outputs of one ormore language models. The other criteria discussed above are examples ofcriteria that may be used. In these and other embodiments, theadditional criteria may be used to determine alignment of tokens andimprove the voting process 1408, as well as being used for otherpurposes. Alternatively or additionally, one or more of the additionalcriteria may be used together.

In some embodiments, other criteria may include one or more of thefeatures described below in Table 5. These features may be used alone,in combination with each other, or in combination with other features.

TABLE 5 1. Account type (e.g., residential, IVR, etc., see Table 10)determined for the speaker, or second user, being transcribed. Theaccount type may be based on a phone number or dev ice identifier. Theaccount type may be used as a feature or to determine a decision, forexample, by automating all of certain account types such as business,IVR, and voicemail communication sessions. 2. The subscriber, or firstuser, account type. 3. The transcription party's device type (e.g.,mobile, landline, videophone, smartphone app, etc.). It may include thespecific device make and model. The specific dev ice make and model maybe determined by querying databases such as user account or profilerecords, transcription party customer registration records, from alookup table, by examining out-of-band signals, or based on signalanalysis. 4. The subscriber's device type. This may include thecaptioned phone brand, manufacture date, model, firmware update number,headset make and model, Bluetooth device type and model, mode ofoperation (handset mode, speakerphone mode, cordless phone handset,wired headset, wireless headset, paired with a v ehicle, connected to anappliance such as a smart TV, etc.), and version numbers of models suchas ASR models. 5. Historical non-rev oiced ASR system or revoiced ASRsystem performance estimated from past communication session involvingone or more of the calling parties on the current communication session.In a first example, the average estimated accuracy, across alltranscribed parties, when transcribing communication sessions for thefirst user may be used as a feature. In a second example, the averageestimated accuracy when transcribing a particular second user during oneor more previous communication sessions may be used as a feature. Animplementation of a selector that uses the second example of thisfeature may include: a. Transcribe a first communication session with aparticular transcription party and estimate one or more firstperformance metrics such as ASR accuracy. b. At the end of thecommunication session, store at least some of the first performancemetrics. c. A second communication session with the same transcriptionparty is initiated. d. The selector retrieves at least some of the firstperformance metrics. e. The selector uses the retrieved firstperformance metrics to determine whether to start captioning the secondcaptioned communication session with a non-revoiced ASR system, arevoked ASR system, or combination thereof (see Table 1). f. Atranscription unit generates a transcription of a first portion of thesecond communication session. g· During the second communicationsession, the selector uses the retrieved performance metrics andinformation from the second communication session to select a differentoption of the non-revoiced ASR system, a revoked ASR system, orcombination thereof for captioning a second portion of the secondcommunication session. Examples of information from the secondcommunication session may include an estimated ASR accuracy, anagreement rate between the non-revoiced ASR system and a revoked ASRsystem and other features from Table 2, Table 5, and Table 11. 6.Historical non-revoiced ASR system or revoiced ASR system accuracy forthe current transcription party speaker, who may be identified by thetranscription party's device identifier and/or by a voiceprint match. 7.Average error rate of the revoked ASR system generating thetranscription of the current communication session or the revoiced ASRsystem likely to generate the transcriptions for the currentcommunication session if it is sent to a revoiced ASR system. The errorrate may be assessed from previous communication sessions transcribed bythe revoiced ASR system or from training or QA testing exercises. Theseexercises may be automated or may be supervised by a manager. 8. AverageASR error rate, estimated from past accuracy testing. 9. A measure ofthe processing resources used to transcribe audio for the currentcommunication session. Resources may be measured, for example, by CPUload, memory usage, the number of active arcs in a decoder search,processing time, instruction cycles per second or per speech analysisframe, processing resources used by a specified ASR sub-process, etc.10. Average error rate of a group of revoiced ASR system or across allrevoiced ASR systems. 11. Estimated ASR accuracy, confidence, or otherperformance statistic for the current session. This performancestatistic may be derived from a figure reported by the ASR system orfrom an estimator using one or more input features, such as from Table 2and Table 5. ASR performance may include word confidence averaged over aseries of words such as a sentence, phrase, or turm. a. The performancestatistic may be determined for an ASR system. b. The performancestatistic may be determined from a fused transcription, where the fusioninputs include hypotheses from one or more revoiced ASR system and/orone or more non-revoiced ASR system. c. The performance statistic mayinclude a set of performance statistics for each of multiple ASR systemsor a statistic, such as an average, of the set of performancestatistics. 12. A log-likelihood ratio or another statistic derived fromlikelihood scores. An example may be the likelihood or log likelihood ofthe best hypothesis minus the likelihood or log likelihood of thenext-best hypothesis, as reported by an ASR system. In the case of ahypothesis containing multiple words, this feature may be computed asthe best minus next-best likelihood or log likelihood for each word,averaged over a string of words. Other confidence or accuracy scoresreported by the ASR system may be substituted for likelihood. 13. Thefollowing features may be used directly or to estimate a featureincluding an estimated transcription quality metric: a. Features derivedfrom the sequence alignment of multiple transcriptions. For example,features may be derived from a transcription from a non-revoiced ASRsystem aligned with a transcription from a revoiced ASR system. Examplefeatures include: i. The number or percentage of correctly aligned wordsfrom each combination of aligned transcriptions from non-revoiced ASRsystems and revoiced ASR systems. The percentage may refer to the numbercorrectly aligned divided by the number of tokens. “Correctly aligned“may be defined as indicating that tokens in a token group match when twoor more hypotheses are aligned. ii. The number or percentage ofincorrectly aligned tokens (e.g., substitutions, insertions, deletions)from each combination of aligned transcriptions from non-revoiced ASRsystems and revoiced ASR sy stems. b. The following features may bederived using a combination of n-gram models and/or neural networklanguage models such as RNNLMs. The features may be derived either froma single ASR system hypothesis transcription or from a combination oftranscriptions from non-revoiced ASR systems and/or revoiced ASRsystems. For example, the features may be derived from multiple n-gramlanguage models and multiple RNNLM models, each with at least onegeneric language model and one domain-specific language model. i.Perplexity, such as the average word perplexity. ii. The sum of wordprobabilities or log word probabilities. iii. The mean of wordprobabilities or log word probabilities, where the mean may bedetermined as the sum of word or log word probabilities divided by thenumber of w ords. c. The following part of speech (POS) features derivedfrom transcriptions from non-revoiced ASR systems and/or revoiced ASRsystems as determined using a POS tagger: i. The percentage of contentwords. Content words may be defined as words representing parts ofspeech defined as content words (such as nouns, verbs, adjectives,numbers, and adverbs. but not articles or conjunctions). Alternatively,content words may be classified based on smaller word subcategories suchas NN, VB, JJ, NNS, VBS, etc., which are symbols denoted by one or moreexisting POS taggers. ii. Conditional probability or average conditionalprobability of each word's POS given the POS determined for one or moreprevious and/or next words. For example, if a word's POS is POS2, theprevious word's POS is POS1, and the next word's POS is POS3, theconditional probability for the word's POS is P(POS2 | POS1, POS3). Theaverage conditional probability may be the conditional word POSprobability averaged over the words in a series of words such as asentence. iii. Per-word or per-phrase confidence scores from the POStagger. d. Lexical features derived front transcriptions frontnon-revoiced ASR systems and/or revoiced ASR systems, such as: i.Lexical diversity, which may be the number of unique words divided bythe total number of words. ii. Percentages of fricatives, liquids,nasals, stops, and vowels. iii. Percentage of homophones ornear-homophones (words sounding nearly alike). e. Time and frequencydomain representations of the audio signal. For example, theserepresentations may be provided as input to a neural net or used asinput to an estimator or classifier for purposes of. for example,estimating confidence, accuracy, speaker intelligibility, andnon-revoiced ASR system/revoiced ASR system selection. Representationsmay include: i. Audio samples. ii. Complex DFT of a sequence of audiosamples. iii. Magnitude and/or phase spectrum of a sequence of audiosamples obtained, for example, using a DFT. iv. MFCCs and derivativessuch as delta-MFCCs and delta-delta-MFCCs. v. Energy, log energy, andderivatives such as delta log energy and delta-delta log energy. vi.Probability that speech is voiced, based on an analy sis of the speechwaveform. The analysis may include a measure of periodicity. 14. Anagreement rate between a non-revoiced ASR systems and a revoiced ASRsystem. 15. An agreement rate between two or more revoiced ASR systems.Example 1: measure the agreement rate between two revoiced ASR systems.Example 2: fuse transcriptions from two or more revoiced ASR systems tocreate a higher-accuracy transcription, then measure an agreement ratebetween the higher-accuracy transcription and one or more other revoicedASR systems. For an example, see FIG. 47. 16. An agreement rate betweentwo or more ASR systems. See FIG. 21. 17. Estimated likelihood or loglikelihood of the transcription, given a language model. For example, alanguage model may be used to estimate the log conditional probabilityof each word based on previous words. The log conditional probability ,averaged ov er all words in the transcription, may be used as an averageestimated log likelihood. 18. An estimate of the difficulty intranscribing the current communication session. 19. Estimated complexityof the conversational topic based on a topic classifier and/or topicdifficulty estimator. 20. A text complexity measure such as informationtheory entropy of the transcription, when evaluated with a languagemodel. 21. A measure of semantic complexity, such as lexical density, ofthe transcription. 22. A Flesch-Kincaid reading ease score, applied tothe transcription. 23. The number or percentage of transcribed wordswith confidence, as reported by a recognizer, greater than a giventhreshold. For example, this metric may count the percentage ofrecognized words with a confidence score greater than 50%. 24. Spectraltilt, or average slope of the magnitude spectrum that may giv e anindication of size or distortion, such as voices sounding muffled, for achannel carrying audio of the communication session. 25. A detectedchange in the speaker, such as when one speaker hands a device to a newspeaker and the new speaker begins to talk. 26. The level of backgroundnoise as measured by a signal-to-noise ratio or noise loudness. 27. Afunction related to signal quality . The function may be responsive tonoise level, interference from other signals, signal distortion such asclipping, spectral shaping or filtering, echoes, reverberation, anddropouts. 28. An indicator of whether the audio signal contains silence,speech, or non-speech energy. This may be used, for example, in adecision to use non-revoiced ASR systems to generate a transcription ofcommunication session segments that appear to include predominantlysilence or non-speech so that a CA of a rev oiced ASR system is lesslikely to waste time listening to audio that does not need transcribing.This indicator may include statistics such as the percentage of theaudio signal determined to be silence and average signal energy level ofa segment of audio. 29. Average, variance, or other statistics derivedfrom the probability that a frame of audio is voiced. For example, thepercentages of the audio signal that is voiced, unvoiced, or silent maybe features. The probability that a frame of audio is voiced may bederived, at least in part, from the height of a peak in anautocorrelation function of tire audio signal divided by the energy ofthe audio signal. 30. Estimated channel or voice quality (e.g., muffled,echoes, static or other noise, distorted). Some elements of thisestimate or classification may use an objective estimator such as ITUP.862. 31. Estimated speaking rate, such as rate in syllables persecond. 32. Estimated speaking clarity of the voice. 33. Average,variance, range, or other statistics of the voice pitch or fundamentalfrequency. 34. Estimated accent type and accent strength of the speaker.35. One or more parameters reflecting an automated assessment of theemotional state (angry, excited, etc.), personality (energetic, tense),or demeanor of the speaker. 36. Speaker characteristics and demographicssuch as age, age categoiy (e.g., elderly, children, a child under theage of 13. legal minor), location, ethnicity, speech impairment, hearingimpairment, and gender. This information may be, for example, obtainedvia customer records, audio analysis, or image analysis of the speaker'spicture or video. 37. A level and type of the speaker's disability orspeech impairments such as stuttering or slurring. The disability andspeech impairment may be detected automatically by examination of thespeaker's voice or it may be determined via lookup in a registry orpatient record. 38. Level and type of hearing impairment of the user ofthe transcription service. 39. An indicator, such as from a silencedetector or by observing that a non-revoiced ASR system is generatingtext while the revoiced ASR system is not, that suggests a CA ofthe-revoiced ASR system has stopped speaking. 40. A second user, such asthe second user 112 of FIG. 1, geographic location as determined by, forexample, IP address, GPS location, cell tower location, ANI, DNIS,customer record, street address, or billing address. 41. The seconduser's accent or dialect based on location or signal analysis ofcommunication session audio. 42. Geographic/accent metrics as in #40 and#41 above, but for the first user, such as the first user 110 of FIG. 1.43. Settings, such as amplification levels, font size, and time zone,the first user has configured for the captioned phone. 44. Networkstatistics such as packet loss or noise levels that may affect speechquality. 45. The compression rate, audio bandwidth, and CODEC type(e.g., Speex, AMR, MP3, G.719, Opus, GSM, G.711) that may affect speechquality. 46. The transcription from the non-revoiced ASR system and/orrevoiced ASR system. Text of the transcription may be a feature. Thetext may, for example, be represented as words or word embeddings. 47.First user account status and history, such as number of times he/shecalled to complain, number of communication sessions to customer care ortechnical support, number of months as a user, payment history andstatus, and credit rating. For example, a first user may receive adifferent class of service depending on the account status. For example,the first user may receive service from a non-revoiced ASR systeminstead of a revoiced ASR system if a payment is overdue. 48. A randomnumber or pseudo-random number such as a hash of the first user's and/orthe second user's phone number or device identifier. This may be used,for example, in selecting samples for quality testing, data collection,or in providing a giv en class of service to a random subset of users. Arandom number may also be used as input to a generative model such as agenerative adversarial network used, for example, as an estimator orclassifier. 49. Flags indicating a special communication session typesuch as whether the communication session is a test communicationsession, a production communication session, a revenue-producing orbillable communication session, a non-revenue producing or non-billablecommunication session, a communication session to be used for measuringperformance, a customer support, technical support, or other customercare communication session, a communication session designated to test anon-revoiced ASR system, a communication session designated to test arevoiced ASR system, a communication session designated to be directedto a specific revoiced ASR system, a communication session designated tobe directed to a specific non-revoiced ASR system or an automatedcommunication session where one or both calling parties are machines.50. Flags indicating recording status, including whether thecommunication session content (e.g., audio, text, n- grams) is being ormay be recorded and what is being recorded. 51. An indication of whethera prompt has been or will be played advising a caller that communicationsession content may be used or recorded. 52. An indicator of whether thesystem has consent to use communication session content, for whichcaller, and which type of consent has been granted. 53. An indicator ofwhether the system has legal clearance to use content from thecommunication session, what content may be used, and in what maimer itmay be used. 54. An indicator of which of the first and second usersinitiated the communication session. 55. An indicator of whether thefirst user has called the second user before, how many times, and howlong ago. 56. An indicator of whether the second user has called thefirst user before, how many times, and how long ago. 57. A featurecorresponding to the second user's name, such as may be extracted fromthe profile or account record. For example, the feature may be a flagindicating that the first or last name on the profile or account islikely to be foreign or that the first name is likely female. 58. Thepause-to-talk ratio or percentage of time a speaker talks. In onescenario, this feature includes time when another speaker is talking. Inanother scenario, this feature excludes time when another speaker istalking. 59. The percentage of time the first user talks compared to thesecond user. 60. Features from Table 2 or Table 5 that are transformedusing nonlinear functions such as sigmoid, hyperbolic tangent, or ReLUfunctions. 61. Features from Table 2 or Table 5 as estimated over one ormore previous communication sessions with the same first user and/orsecond user. Once a given communication session ends, features such astopic type, ASR accuracy, etc., that pertain to characteristics of thecommunication session may be stored in a database for use with futurecommunication sessions. Access to this previous communication sessioninformation may be limited to specific individuals such as the firstuser or other parties to the communication session. 62. Historical orprojected communication session length for the first user, based, forexample, on one or more previous communication sessions. Communicationsession length may be measured, for example, in units of time (such asin seconds) or in words. 63. Historical or projected communicationsession length for the second party, based, for example, on one or moreprevious communication sessions. 64. A current communication sessionlength. In a first use case example, current communication sessionlength may be used to predict accuracy if accuracy tends to change overthe length of a communication session. In a second use case example, afirst period of time, such as 20 seconds, may be particularly important,so the system may increase the likelihood of sending, for example, thefirst period of time to a revoiced ASR system. Conversely, if thelikelihood is high that the start of a communication session includessilence, such as with voicemail communication sessions, a first periodof time, such as the first 10 seconds, may be captioned using a non-revoiced ASR system. In a third use case, an estimator or selector maypredict that a CA of a revoiced ASR system may experience fatigue as thecommunication session progresses and increase the likelihood oftransferring the communication session to a non-revoiced ASR system forlonger communication sessions. 65. Time of day, day of week, orindicators for holidays. 66. Detection of signals and messages such asanswering machine beeps, a special information tone (SIT), communicationsession progress tones, signals, or messages (ringing, busy, answer,hang-up), and SIP (Session Initiation Protocol) messages. 67. Anindicator of the language used by the first and/or second user. Thisindicator may be derived from records associated with the user's profileor account, an estimate of the user's language based on the user's nameas derived from the user's profile or account or a reverse directorylook up based on the user's telephone number. The indicator may bederived from language detection software that determines a spokenlanguage based on analysis of the user's audio. 68. A statistic derivedfrom the number of corrections a CA client obtains from a text editor.For example, the number or average number of corrections made during aspecified time period such as a minute, a day, or a communicationsession may be a feature. 69. An estimate of which set of models willprovide the best transcription for the current communication session.The estimate may be based on account type, signal analysis, knowledge ofuser history, trying multiple ASR models, and other factors such asthose in Table 2 and Table 5. 70. A prediction of the cost of varioustranscription methods (see Table 1) and a prediction of transcriptionaccuracy for various transcription methods. In one variation, thisfeature set may include a prediction of which transcription method willcost least and still meet established accuracy standards. 71. Severityof an ASR error. See FIG. 57. 72. The type of plan the first user issubscribed to. For example, if the first user has a premium serviceplan, the selector may favor sending communication sessions for thefirst user to an ASR system, such as a revoiced ASR system, thatdelivers relatively higher accuracy, or the selector may send allcommunication sessions for the first user to systems with relativelyhigher accuracy. The following may also be used as features, if thecurrent first user does not have an account, is not a subscriber, or isnot certified eligible to receive transcriptions, or if the user isunknown or has not logged in. 73. The medical history or other statusassigned to the first user. For example, the first user may have specialneeds that require transcriptions from a revoiced ASR system or from arevoiced ASR system that obtains revoiced audio from a CA with specialskills. As another example, the first user may be a test number,voicemail user, or hearing subscriber, with low priority that may betranscribed by a non-revoiced ASR system. 74. The first user's degree ortype of hearing loss or need for transcriptions. An example of how thisfeature may be used is to select a non-revoiced ASR system if the needis low. 75. The number of communication sessions the first user hasplaced or the number of minutes the first user has used over a period oftime, such as during the current or previous month. As a use caseexample for this feature, the first user may receive service for aparticular period of time, such as 60 minutes, at a first quality level,one that may use more revoiced or more expensive ASR system resources,and thereafter at a second quality level such as service provided bynon-revoiced ASR system. 76. The importance or priority of thecommunication session. For example, high-priority numbers may includeemergency numbers such as 911 communication sessions, police, fire,ambulance, poison control, etc., communication sessions to medical orlegal providers, and parties identified as high-priority by a first user(or authorized representative). High priority communication sessions mayalso include communication sessions for which transcription generationmay be difficult (for example, because the speech or signalcharacteristics render the audio less intelligible) or correspond to adevice identifier that has been identified by a user as high priority.In some embodiments, high-priority numbers may be sent to a revoiced ASRsystem or may be more likely to be sent to a rcv oiccd ASR system. 77.An indication that a communication session has been selected to beprocessed using high-accuracy transcription methods for purposes such asdata collection or model training. For example, in ASR model training, adata collection scheduler may identify a percentage of all communicationsessions at random or based on communication session characteristics tobe sent to revoiced ASR systems so that the audio and transcription forthe communication session may be used for a step in model training thatrequires enhanced accuracy (see FIG. 64). 78. The total number ofcommunication devices connected to the communication session. 79. Aconstant value. This feature may be used, for example, in an estimatorincluding a weighted sum, as an offset or constant correction factor.80. Information extracted from the first user's account, user record, orprofile such as name, phone number, age or birthdate, user preferences,an indication of the account type (business, residential, government,etc.), an identifier for the first user's company or enterprise (e.g.,for corporate accounts), identity of the user or users authorized toreceive the captioning service, username and password, voiceprint, dateof start of subscription, contact list or address book contents, speeddialing list, pictures of contacts, and calling history including phonenumbers, times and dates, communication session duration, and which userinitiated each communication session. 81. An indicator of whether thefirst user is an authorized subscriber. The indicator may be based onthe user entering credentials such as a login name, PIN, or password.The indicator may be based on facial recognition, a fingerprint match,the user's voice matching a voiceprint, the user's language usage (e.g.vocabulary or pattern of words), or other biometrics. In someembodiments, the indicator may be used to provide a first level ofservice such as transcription by a revoiced ASR system if the user isauthorized and a second level of service such as transcription by anon-revoiced ASR system otherwise. In another embodiment, the indicatormay be used to allow transcription generation for authorized users. Inanother embodiment, the indicator may be used to report unauthorizedusage. 82. Signal analysis of the communication session audio to detectfeatures such as tone (shouting, whispering), volume (loud, quiet,distant), an indication of multiple people speaking at once, and noisetypes (music, singing, wind, traffic, radio or TV, people talking,etc.). 83. The length of time since the beginning of the work shift fora CA revoicing audio. This metric may be used as an estimator offatigue. 84. The service type or class of service being provided.Examples of service types include transcribing communication sessions,conducting surveys, labeling data, transcribing videos, etc. Further,each type of service may have multiple classes, which may also befeatures. For example, a communication session transcription service mayoffer multiple classes such as various levels of accuracy, variouslanguages, various latency requirements, various degrees of security,and various specialized skills such as competence in medical, legal, orother industry- or topic-specific terminology. 85. An indicator ofurgency or when a task needs to be completed, such as whether a task isneeded in real time or may be performed offline. For example, if a groupof one or more transcription units provides a first service transcribingcommunication sessions in real time for phones where a short responsetime is required (e.g., a few seconds) and a second service transcribingrecorded communication sessions where a longer turnaround time (e.g., afew hours) is allowed, then an indicator of whether a task belongs tothe first or second service may be used to make a non-revoiced ASRsystem/revoiced ASR system selection and/or to defer non time-criticalwork to a time when more of the desired transcription resources areavailable. In another example, if non-revoiced ASR systems are in shortsupply, then the decision to send the task to a revoiced ASR system orwait for an available non-revoiced ASR system may depend on the urgency.For example, if the indicator signals that a task is needed quickly andno revoiced ASR systems are available, the task may be directed to anon-revoiced ASR system. If multiple tasks require non-revoiced ASRsystems and/or revoiced ASR system resources, the process of schedulingresources may be responsive to the relative urgency of the tasks. 86. Anindicator of the type and nature of various tasks that are waiting to becompleted. For example, if non- revoiced ASR system or revoiced ASRsystem resource is available, in addition to those resources currentlyused to transcribe ongoing communication sessions, and there is a queueof offline transcription tasks to be completed, then a task from thequeue may be directed to the available resource. 87. An alignment scorebetween two or more transcriptions. For example, a disagreement rate,agreement rate, edit distance or Levenshtein distance between twotranscriptions may be a feature. In one scenario, one transcription maybe from a non-revoiced ASR system and another from a revoiced ASRsystem. In another scenario, the two or more transcriptions may be fromnon-revoiced ASR systems. 88. The output of an estimator, classifier, orselector. 89. The identity of the transcription party and/or thesubscriber. An identity may include, for example, an account number, aname and phone number, a device identifier, or a voiceprint and a deviceidentifier. In embodiments where devices are shared among multipleusers, a single device identifier may correspond to multipletranscription party identities. 90. A function derived from thetranscription party's identity, phone number, or device identifier; thesubscriber's identity, phone number, or device identifier; or acombination thereof. For example, communication sessions where thetranscription party's phone number matches a first regular expressionand/or where the subscriber's phone number matches a second regularexpression may be transcribed using a non-revoiced ASR system. In acounterexample, matching communication sessions may use a revoiced ASRsystem. In another example, communication sessions where thetranscription party's identity or device identifier match entries in aselected list such as a list of names and/or phone numbers, may betranscribed using a non-revoiced ASR system. In a counterexample,communication sessions matching entries in the list may use a revoicedASR system. 91. The average confidence of transcriptions for one or morespeech segments, where a speech segment includes one or more words. Atranscription of a speech segment may. for example, be a section of textdelivered as a group by an ASR system. For example, a confidence scoreaveraged over each of the k (where k may be 1, 2, 3, among othernumbers) most recent segments determined by an ASR system may be used asa feature. In an example application of this feature, if the averageconfidence of the past k segments drops below a selected threshold, arevoiced ASR system may be subsequently used to generate transcriptionsfor the communication session. 92. An analysis of communication sessioncontent to determine, for example, the degree of difficulty the firstuser is having understanding the transcription party. The analysis mayproduce, for example, the frequency of phrases such as “What?“ “I'msorry.“ or “Huh?“ from the first user and phrases such as “Did you hearthat?“ or “I said... “ or repeated or rephrased utterances from thetranscription party. The degree of difficulty may be used, for example,as a feature indicating captioning errors or delays, to influence thenon-revoiced ASR system/ revoiced ASR system decision, in estimatingaverage non-revoiced ASR system and/or revoiced ASR system accuracy, toprovide feedback to a CA providing revoiced audio to a revoiced ASRsystem such as advising the CA on his/her performance, creating CAperformance reports, and to generate alerts. 93. An analysis ofcommunication session audio and/or transcriptions to determine how muchof the conversation a first user is understanding. The communicationsession audio and/or transcription may, for example, be input to amachine learning system trained to estimate a first user's level ofcomprehension. 94. The number of words in a hypothesis transcription.95. The number of words in a reference transcription. 96. An alignmentlength, which may be the total number of token columns created by analignment between two or more transcriptions. For example, if thehypothesis “the quick brown“ is aligned with “quick brown fox“ so thatthe tokens “quick“ are matched and the tokens “brown“ are matched, thealignment length may be the number of words in the string “the quickbrown fox,“ which is four. 97. The subscriber's technical configuration.This may include, for example, the method used to connect the subscriberto the transcription system (e.g. wired Internet, hotspot, smartphone),the nature and identity of the subscriber's communication (e.g.telephone) provider and Internet service provider, location of the ASRsystems (e.g., ASR system on the device of the subscriber, ASR system ata specified captioning center, etc.), whether transcriptions are on oroff by default, etc. 98. An indicator by a user that the user wants aservice other than or in addition to transcriptions. For example, theuser may press a button or click an icon to request action from avirtual assistant or may ask for a service such as the time, a reminderor wakeup call, customer service, playing music or videos, checkingvoicemail, initiating a communication session, asking for information,or other services that may be provided by a virtual assistant. 99.Features derived from a fusion process. For example, the number orpercentage of times a word from a first ASR system is selected by avoting process 1408 instead of a word from a second ASR system may beused as a feature to estimate accuracy of the first ASR system. 100. Aposition detected for a handset or microphone providing audio to betranscribed. For example, the position and angle of a handset, thelocation of a microphone relative to the speaker's mouth, and adetermination of whether a speaker is holding a handset in his/her leftor right hand may be used as features. 101. An indication that a wordmay be preferred for a particular ASR system or that a first ASR systemis more likely to correctly recognize the word than a second ASR system.This feature may be used, for example, in voting. For example, a list ofwords may be created that are believed to be more reliably recognized bythe first ASR system. If the first ASR system recognizes a first word onthe list and a second ASR system recognizes a second word, the votingprocess 1408 may select the first word in response to its presence onthe list. In another example, each word in the list may be associatedwith a weight. The voting process 1408 may use the weight as a featurein determining whether to use a word from the first or second ASRsystems. In another example, a first ASR system may be configured todetect a list of words including, for example, filler words, spokenpunctuation, quickwords, and profanity more reliably than a second ASRsystem. In this example, the voting process 1408 may select a listedword from the first ASR system over an alternative hypothesis from thesecond ASR system. 102. A confidence score of one or more wordstranscribed by first ASR system that has received a grammar from asecond ASR system. 103. All the features listed in Table 2.

As discussed above, in some embodiments, another criteria the votingprocess 1408 may consider when selecting tokens from token groups isoutputs of ASR models. For example, the output of models, such as errortype models and language models, may be considered as the othercriteria. In these and other embodiments, a prior probability specifiedby the language model for the tokens may be used to select a token froma token column in addition to the number of times (“counts”) a tokenappears in a token group.

In these and other embodiments, the tokens may each be weighted based onthe language model probabilities associated with the tokens. In theseand other embodiments, weighting the tokens may increase the likelihoodthat a result rated by the language model as more probable may be chosenduring the voting process 1408. Weighting the tokens may includemultiplying a token count for each token in a token column by theprobabilities from the language model or adding the probabilities fromthe language model to the token counts for each token in the tokencolumn to determine a score used in the voting process 1408. The tokencounts may be further weighted by other factors such as wordprobabilities and confidences estimated by ASR systems.

For example, suppose that, in the “ok let's meet at 4” example providedabove with the token columns reproduced below:

1. o k — let us meet — more — 2. o k — says — meet at four o'clock 3. ok ha let us meet at far —the three hypotheses, up until the second-to-last column, are fused toform “ok let us meet at.” The language model may output the probabilityfor each of the tokens in the second-to-last token column as follows.

P(four)=0.05

P(more)=0.01

P(far)=0.02

In these and other embodiments, the voting process 1408 may multiply thecount of each token by the probability. Thus, the token count for “four”may be 0.05, the token count for “more” may be 0.01, and the token countfor “far” may be 0.02. The token “four” may have the highest probabilityand may be selected so that the fused hypotheses forms “ok let us meetat four.”

In some embodiments, the language model may indicate a probability of asequence of N tokens. In these and other embodiments, the probability ofa sequence of N tokens may be used to indicate the probability of atoken given a context that is based on one or more tokens directlypreceding the token. For example, a trigram language model may indicatethe probability (or a form thereof, such as log probability) of threetokens in a sequence and thus the probability of a token given twotokens directly preceding the token. For example, the language model maydetermine the probability of the token “bread” followed by the tokens of“loaf” of as P(bread|loaf of)=0.84, where P(<token>|<(N−1) tokens>) isthe probability of a token given the preceding N−1 tokens, where N isthe number of words in the sequence. N may be any positive integer, forexample, 1, 2, 3, 4, 5, 8, 10, or 15. In the example above with respectto phrase “ok let's meet at 4”, N may equal four and a language modelmay specify the following probabilities:

-   -   P(four|us, meet, at)=0.05    -   P(more|us, meet, at)=0.01    -   P(far|us, meet, at)=0.02

In some embodiments, back-off probabilities may be used in cases whereprobabilities for N words are not available, but where statistics forN−1 words are available. For example, if there are N words in asequence, the language model may not include a probability for asequence with N words for each of the tokens in a token column. In theseand other embodiments, the language model may include a probability fora token based on a sequence that is N−1 tokens long.

Additionally or alternatively, the language model may indicate theprobability of a token given one or more preceding tokens and one ormore subsequent tokens for at least one input hypothesis. For example,suppose a first input hypothesis ends with the sequence “meet you at thetrain station at four” and a second input hypothesis ends with thesequence “meet you at the trade.” A language model may be used to helpdecide between the tokens “train” and “trade.” In the trigram exampleprovided above, a trigram probability may depend only on the previoustwo tokens, “at” and “the.” In contrast, in these and other embodiments,a probability may further depend on the subsequent token “station.” Inthese and other embodiments, the probabilities for each hypothesis maybe determined based on one of the hypotheses using the subsequent tokenand the other hypothesis not using the subsequent token. Alternativelyor additionally, the probabilities for each hypothesis may be determinedbased on a hypothesis without the subsequent token being added to thehypothesis. For example, the hypothesis of “meet you at the trade” maybe changed to “meet you at the trade station.” For example, theprobabilities may then be written as P(“train”|prior=“at the”,future=“station”) and P(“trade”|prior=“at the”, future=“station”). Theresulting probabilities may be used to help decide between selecting thetoken “trade” or “train.”

In some embodiments, the voting process 1408 may use a probability basedon preceding and/or subsequent tokens to reduce latency for cases wherevarious transcription units provide transcriptions with differentdegrees of latency. Using probabilities based on preceding andsubsequent tokens may be advantageous because knowledge of subsequenttokens provided by faster transcription units may be combined withknowledge of previous tokens provided by slower transcription units.Transcription units with different degrees of latency may include atranscription unit with a non-revoiced ASR system with relatively lowerlatency and a transcription unit with a revoiced ASR system withrelatively higher latency.

Continuing the example of using subsequent tokens, the voting process1408 may encounter multiple ties in a row. For example, the followingtwo hypotheses, shown here in an aligned form, have five ties in a row:

1. I like to walk my favorite dog 2. I often — talk to — dog

In this example, suppose the align text process 1406 and voting process1408 is at a decision point (a.k.a. the current node) to decide betweenthe token ‘like’ and ‘often.’ In some embodiments, the align textprocess 1406 and voting process 1408 may do a full search of all ofpossible combinations from the point of the tie (e.g., “like” or“often”) until there is consensus again (at “dog”). In the exampleabove, the voting process 1408 may determine the likelihood, given thecontext of prior and various combinations of subsequent words, of eachpossible sequence of tokens such as “I like to talk to dog” and “I oftenwalk my favorite dog.” A language model scoring technique such asbackoff or Kneser-Ney smoothing may be used to select the most probablesequence for inclusion in the fused transcription.

The align text process 1406 and voting process 1408, in searching allpossible combinations, may require a significant amount of processing tocomplete the search. To reduce the processing load, the align textprocess 1406 and voting process 1408 may utilize an alignment method tolimit the length of the search space. For example, if there are ten tiedtokens in a row, the align text process 1406 and voting process 1408 mayexplore combinations of the first five tied tokens to select a more orthe most likely sequence and then repeat the process for the next fivetokens. Additionally or alternatively, the align text process 1406 andvoting process 1408 may reduce the processing load by using a Viterbisearch or other dynamic programming method to find a more or mostprobable sequence.

A language model probability used for fusion may also be conditioned oncontexts from multiple input hypotheses. For example, with two inputs, aword probability may be expressed as P (word|context 1, context 2),where context 1 is one or more previous tokens from a first inputhypothesis and context 2 is one or more previous tokens in a secondinput hypothesis. Context 1 may further include one or more futuretokens from a first input hypothesis. Context 2 may further include oneor more future tokens from a second input hypothesis. Similarly, for amultiple input ASR system such as the embodiments illustrated in FIGS.40 and 41, an ASR system may use a language model with probabilitiessuch as P(word|context 1, context 2, context 3, . . . ) conditioned oncontexts from multiple input hypotheses.

Additionally or alternatively, the voting process 1408 may output tokensbased on the best available information at a point in time. In these andother embodiments, the voting process 1408 may provide corrections iffuture inputs or input changes trigger a change in tokens alreadyoutput. For example, using the example inputs above, the voting process1408 may initially output “meet you at the trade.” After providing theoutput of “meet you at the trade”, the voting process 1408 may determinethat the token “trade” was incorrect after the voting process 1408determines the subsequent token of “station.” In these and otherembodiments, the incorrect output may have been provided to a device ofa user for presentation. In these and other embodiments, the correcttoken may be provided to the device to replace the incorrect token. Insome embodiments, the voting process 1408 may also change a previousoutput in response to an ASR system making a change to a previoushypothesis.

In some embodiments, an error type model may also be used by the votingprocess 1408 to increase alignment and/or voting accuracy. In these andother embodiments, a type of error from multiple different error typesmay be assigned to each token column based on the differences betweenthe tokens in the token column. An error type model may be built thatmay use patterns of error types to assist in selecting tokens from thetoken columns.

As an example of an error type model, consider an example referencetranscription (e.g., what was actually spoken) “Hermits have no peerpressure” and a hypothesis transcription (e.g., what the ASR systemoutput) “Hermits no year is pressure.” An alignment may be arranged withan error type line to create an error map such as:

Reference: hermits have no peer — pressure Hypothesis: hermits — no yearis pressure Error Type C D C S I C

The error type codes may be “D” for deletions, “S” for substitutions,“I” for insertions, and “C” for correct. An error type model for aspeech transcriber may be constructed by presenting a corpus of audiointo an ASR system. The ASR system may output an output transcription.The output transcription may be compared to a reference transcription ofthe corpus of audio by aligning the two transcriptions and comparing thealigned transcriptions to determine the error type for each word groupin the corpus of audio. Based on the comparison, a pattern of errortypes may be used to construct an error type model.

In these and other embodiments, the error type model may include a setof conditional probabilities of given error types given the context ofprevious and/or future error types. For example, the error type modelmay include the probabilities of patterns of error types such as “D”s,“S”s, “I”s, and “C”s that may characterize output of the ASR system.Errors of a transcription by the ASR system may then be provided to theerror type model for estimating or predicting the reliability of thetranscription for purposes of alignment and/or voting. A similar errortype model may be determined for a pair of ASR systems, using the methoddescribed above for an ASR system and a reference transcription. Inthese and other embodiments, the error type model may be built for agiven ASR system using a language modeling method based on, for example,n-grams, or using other machine learning methods such as neuralnetworks.

As discussed above, the align text process 1406 and voting process 1408may be configured to receive a sequence of tokens from each of multipleASR systems. In these and other embodiments, the sequence of tokens mayinclude phrases, words, subword units, or a combination of words andsubword units. Subwords, as used in this disclosure, may refer to partsof words that have been divided into roots, stems, prefixes, andsuffixes (e.g., “reuniting” may be broken into subword units as“re-unit-ing”). Subword units may also include words that are parts ofcompound words (e.g., downtown=down+town). Subword units may alsoinclude syllables, such as may be shown as subdivisions of a word in astandard dictionary (eg. “re-u-nit-ing”). Subword units may also includephonemes or characters.

In some embodiments, the align text process 1406 may be configured toalign the tokens, such that subwords may be aligned as well as words.For example, the phrase “I don't want anything” may be transcribed bythree ASR systems as:

I don't want anything I don't want everything I don't want any seen

In this example, there is a three-way tie for the last token becauseeach hypothesis includes a different word for the last token. However,if words are broken into subwords, each hypothesis includes anadditional token and the token alignment becomes:

I don't want any tiling I don't want every tiling I don't want any seen

The voting process 1408 may then produce the output “I don't wantanything.” Thus, in some embodiments, by using subwords as tokens asillustrated above, a simple majority vote may render the correct set oftokens for output.

In some embodiments, the tokens that represent subwords may be combinedinto whole words during the voting process 1408. For example, during thevoting process 1408 the input hypotheses may be examined and one of thehypotheses that includes the selected token by voting may be used as atemplate for combining the subwords. For example, the first hypothesisin the above example, may be used as the template such that the outputis “I don't want anything” instead of “I don't want any thing” with theword “anything” broken into the subwords “any” and “thing.”

In some embodiments, the align text process 1406 and voting process 1408may not divide words into sub-words where there is significant agreementbetween hypotheses. For segments of hypotheses that lack significantagreement, words may be split into subwords that may be aligned andsubjected to voting and recombination of the subwords. Alternatively oradditionally, the transcriptions generated by the transcriptiongeneration processes 1402 may include words that are divided intosubwords. The transcriptions with the subwords may be provided to thealign text process 1406 and voting process 1408. Alternatively oradditionally, some of the transcriptions generated by the transcriptiongeneration processes 1402 may include words that are divided intosubwords. Other transcriptions that do not include words divided intosubwords may be sent to a division process that may divide one or morewords in the other transcriptions into subwords.

In some embodiments, alignment of hypotheses may be used to determine anaccuracy score for the output of the voting process 1408. For example, ahypothesis and reference may be aligned. A number of differences in thealigned transcriptions may be determined. When alignment is performedwith the hypotheses including subword units, several options formeasuring accuracy may be used. The options may include:

-   -   1. Accuracy may be measured on a word basis, using word error        rate, not subword error rate. For example the word strings “I        don't want anything” and “I don't want everything” differ by one        word out of four, so the accuracy may be determined as 3 correct        out of 4 words=75%.    -   2. Accuracy may be measured on a subword basis, where        differences between the aligned hypothesis and reference subword        strings are counted as errors. For example the strings “I don't        want any-thing” and “I don't want every-thing” differ by one        syllable out of six, so the accuracy may be determined as 4        correct out of 5 subwords=80%.    -   3. Accuracy may be based on a combined word and subword score.        For example, the accuracy may be determined as the average of        the word accuracy and subword accuracy.    -   4. Accuracy may be measured on a word basis, using word error        rate, but when the error map is displayed, it may use subword        and/or word alignment. For example, if a reference “I'm leaving        now” is transcribed as “I'm leaning,” the error map based on        words may appear as:

Reference: I'm leaving now Hypothesis: I'm ******* leaning

-   -   -   But if a subword alignment based on, for example, syllables            or characters, is used to align the displayed result, the            matching subword units (in this case, “ing”) in both            transcriptions may cause words with similar subword units to            be aligned so that the error map may appear in a format            where alignment is responsive to word similarity such as:

Reference: I'm leaving now Hypothesis: I'm leaning ***

-   -   -   Note that both error maps may be correct and represent the            same minimum word edit distance, but that the second may be            easier for a human to read and understand. This improved            alignment format may be used when the error map is displayed            to a human reviewer such as a TLS (see FIG. 56) or judge            (see FIGS. 50 and 52).

In some embodiments, the transcription generation processes 1402 mayeach generate a single hypothesis that may include a sequence of tokensthat may be ultimately provided to the align text process 1406 andvoting process 1408. Additionally or alternatively, the transcriptiongeneration processes 1402 may be configured to generate rich structures,such as word confusion networks (“WCNs”), n-best lists, or lattices,which contain information about alternative hypotheses and may includethe relative probabilities or likelihoods of each. These rich structuresmay be combined to create a consensus hypothesis. In one example,alternative hypotheses embedded in the rich structures may be used tobreak voting ties, evaluate confidence for words, word strings, orsubwords, and result in more accurate hypotheses that may not have beengenerated had only a single hypothesis from each transcriptiongeneration process 1402 been used.

In some embodiments, one or more alternate hypotheses from transcriptiongeneration processes 1402 may be used as additional inputs to the aligntext process 1406. For example, the first transcription generationprocess 1402 a may generate a first hypothesis and a second hypothesis.Both the first and second hypotheses may be provided to the align textprocess 1406 along with the hypotheses from the other transcriptiongeneration processes 1402.

In some embodiments, the align text process 1406 and/or voting process1408 may be configured to utilize a Viterbi search or variation of theViterbi search adapted to measuring edit distance between tokens toalign token sequences. In these and other embodiments, an example of theViterbi search method may include such as the Wagner-Fischer dynamicprogramming method. Additionally or alternatively, other search methodssuch as code implementing Dijkstra's algorithm or an A* (spoken as “Astar”) search method may be used for alignment of tokens.

An example of the alignment process using a Viterbi search method is nowprovided. Assume the align text process 1406 obtains a first hypothesiswith a first sequence of tokens and a second hypothesis that includes asecond sequence of tokens from different ones of the transcriptiongeneration processes 1402. In these and other embodiments, the aligntext process 1406 may find a path that best meets a selected set ofperformance criteria by constructing a two-dimensional grid representingthe first sequence in a first dimension and the second sequence in asecond dimension. The performance criteria may include the lowest costor the highest score. For example, the cost may be a function of thenumber of deletions “D,” substitutions “S,” and insertions “I.” If allerrors receive the same weight, the cost may be represented by D+S+I.The Viterbi path may then chose the alignment between the first andsecond sequence that results in the lowest cost as represented by D+S+I.The highest score may represent the Viterbi path that aligns the firstand second sequences such that a score such as the number of matchingwords, the total path probability, or N-(D+S+I), where N is the numberof words in the reference, is increased.

In some embodiments, the processing time of the Viterbi search may beapproximately proportional to L{circumflex over ( )}R (L raised to thepower of R), where L is the average number of tokens per sequence and Ris the number of sequences. For example, if there are five transcriptiongeneration processes 1402 and each transcription generation process 1402generates a sequence of ten words, the processing time may beproportional to L{circumflex over ( )}R=10,000. In some embodiments, aprocessing load for the Viterbi search may be reduced by using asequential alignment method where the voting process 1408 aligns twoinput sequences to create a first new sequence, then aligns a thirdinput sequence to the first new sequence to create a second newsequence, then aligns a fourth input sequence to the second new sequenceto create a third new sequence, and so on. In these and otherembodiments, the align text process 1406 may be configured to alignfirst the sequences estimated to be highest in accuracy. The accuracydetermination may be based on historical accuracy measured for eachtranscription generation process 1402, an estimate of accuracy for thecurrent transcriptions, or other accuracy metrics, among others. Thesubsequent sequences may be aligned in order of decreasing estimatedaccuracy. As such, the align text process 1406 may sort sequences inorder of decreasing estimated accuracy prior to sequential alignment.

In some embodiments, the align text process 1406 may be configured tofind an alignment between multiple sequences by searching for analignment that reduces a sum of pairs edit distance function. The sum ofpairs edit distance function may include the sum of the edit distancebetween each pair of sequences. For example, if there are threesequences, seq1, seq2, seq3 and an edit distance function d(a,b) whichdetermines the edit distance between sequences a and b, the sum of pairsdistance function may be expressed asd(seq1,seq2)+d(seq1,seq3)+d(seq2,seq3). An example of an edit distanceis the minimum number of changes (insertions, deletions, orsubstitutions) needed to convert a first string to a second string.

In some embodiments, the align text process 1406 may utilize othermethods for finding an alignment between multiple sequences whilelimiting processing of a device performing the align text process 1406.These methods may include any one or combination of the above or belowdescribed methods:

TABLE 6 1. Use a beam search to eliminate alignment paths or nodes wherea performance criterion falls below a selected threshold. 2. Use boundedrelaxation in an A* search to reduce the sum of pairs edit distance. AnA* search uses the function f = g + w*h, where g is the sum of pairsedit distance of the sequences up to the current node, h is anapproximation of the distance to the final endpoint, which may bedetermined as the sum of pairs edit distance of the tokens of thesequences following the current node, and w is a weight variable used toprioritize the search direction. The function f may be computed, forexample, for each node in the neighborhood of the last node in the bestpath determined. The node with the lowest f score may be searched next.When w is relatively small, A* may be more accurate and slower than forrelatively larger values of w. If an initial value of w causes thesearch to take longer than a determined threshold, w may be increasedand the search may be restarted. An A* search may be used in conjunctionwith abeam search. 3. Use a progressive alignment method (also known assequential alignment or the hierarchical or tree method), a heuristicfor multiple sequence alignment comprising a succession of pairwisealignments, stalling with the most similar pairs.. 4. Use a sequentialalignment to generate a second sequence from the input sequences.Reorder the input sequences and repeat to generate a third sequence.Repeat to generate a fourth, fifth, etc., sequence. In a firstembodiment, use sequential alignment to align the new sequences. In asecond embodiment, measure the average edit distance between each newsequence and the input sequences. Select the new sequence with theshortest edit distance. In a third embodiment sequentially fuse the newsequences in order of the shortest edit distance first. 5. Alignmultiple sequences, using words as tokens, for alignment and voting tocreate a first fused transcription. Align the multiple sequences againby first splitting words into subwords, then use subwords as tokens foralignment and voting to create a subword transcription. Convert thesubword transcription to a second word transcription. A subwordtranscription may be converted to a word transcription using methodssuch as: a. Preserve word boundaries when splitting words into subwordsand through the fusion process. Use a dictionary or other lookup tableto convert each sequence of subwords, which may be delimited by wordboundaries, back into words. b. Use a Viterbi or other dynamicprogramming search and a language model to find the most likely sequenceof words matching the subword sequence. Using words as tokens, fuse thefirst fused transcription with the second fused transcription to createa third fused transcription. In various embodiments, subwords mayinclude parts of words such as phonemes, syllables, characters, or wordparts such as roots, bases, stems, prefixes, suffixes, etc. 6. Use wordendpoints from ASR systems as an initial estimate of the alignment byaligning transcriptions in time according to the endpoints. For example,as an initial estimate of the alignment, align word endpoints from eachspeech transcriber. Then refine the alignment using a method such assequential alignment, a beam search, or a constrained search that limitsthe search space to regions in the neighborhood of the endpoints. Ifendpoints are not available from an ASR system, such as from a revoicedASR system, use an approximation method such as one or more of: a.Assign endpoints based on audio length multiplied by the word positionin the transcription, divided by the overall sequence length in words.b. Assign endpoints based on the time text is received from the revoicedASR system, minus a correction factor to account for average processingtime. c. Use endpoints from a second ASR system that provides endpointsand adds a correction factor corresponding to the average time offsetbetween the revoiced ASR system that does not provide endpoints and thesecond ASR system. d. Use an alignment ASR system to generate endpointsin the event the ASR system does not produce useful endpoints. Forexample, the transcription output from a revoiced ASR system may definea grammar for the alignment ASR system. With the CA voice as input, thealignment ASR system may recognize tire text defined by the grammar andgenerate endpoints. The grammar may constrain the alignment ASR systemto recognizing substantially the same text as what the revoiced ASRsystem generates, so the alignment ASR system runs quickly and withfewer hardware resources. This operation by the alignment ASR system maybe described as a “forced decision” mode, since the text output ispredetermined by the grammar. In one scenario, the denormalizedtranscription from the ASR system may also be used as an input to thefusion steps of alignment and voting.

In some embodiments, fusion results generated by the align text process1406 and voting process 1408 may be recomputed frequently, such as whilethe transcribed party is talking. The fused transcription, for example,may be recomputed each time a new token is received from one of thetranscription generation processes 1402, periodically at shortintervals, or once a certain amount of audio has been received. In someembodiments, the align text process 1406 and voting process 1408 may runeven though the transcribed party has not necessarily stopped talking oreven finished a sentence. In these and other embodiments, performing thealign text process 1406 and voting process 1408 while the transcribedparty is not finished talking may be referred to as providing partialresults. In these and other embodiments, the partial results may bedetermined by fusing the transcriptions that have been received.

In some embodiments, partial results may be obtained by evaluatingsubstantially all input, including text input, confidence estimates,endpoints, etc., received from the start of a communication session, orstart of the transcription session, from the point where a transcribedparty begins speaking to the current point in time, or from the pointwhere a transcribed party begins speaking and has continuously spoke tothe current point in time.

In some embodiments, a point in time tf is established to denote thepoint in time before which fusion results are unlikely to change, evenwith further audio input into or transcription output from thetranscription generation processes 1402. Fusion output before tf may bereferred to as “locked.” When fusion results are evaluated, only resultsafter tf may be determined because results before tf may not be expectedto change. As such, alignment results before tf may have already beenfused and sent to the vote process 1408 and to the first device 104. Theevaluation after tf may also be simplified because, as with a beamsearch, only alignment paths that include locked results may beconsidered. All other paths, in some embodiments, may be eliminated fromthe search.

An example of locking results in an alignment search may be illustratedby Matrix 0 below where two sequences, “I like apples and bananas” and“I might apples bananas sauce” are aligned. In this example, a word froma column is considered aligned with a word in a row if there is an “x”in the corresponding column and row. The alignment search is the processof finding the alignment, or pattern of “x” s, that best matches wordsbetween sequences. The “path” may be considered to be the sequence ofcells marked with an “x.” In an embodiment of a Viterbi search, linksmay be formed between cells by analyzing one column at a time, movingfrom left to right (the forward pass). Links may indicate the bestoption for the previous match) and point backwards from a given cell tothe best previous match. For example, there may be a link from row 2,column 2 back to row 1, column 1, since row 1, column 1 is the finalcell of the best alignment path to this point in fusing process. Afterthe links are in place to a particular column, a backtracking step (thebackwards pass) may follow the links from right to left, starting at theparticular column, to determine the path, which may be used to definethe alignment between sequences.

Sequence 1 I like apples and bananas Row: Sequence 2 I X — — — — 1 might— X — — — 2 apples — — X — — 3 bananas — — — — X 4 sauce — — — — — 5Column: 1 2 3 4 5

Suppose, in the example above, that the alignment of a portion, “I likeapples,” of sequence 1 and a portion, “I might apples,” of sequence 2 isdetermined by align text process 1406 to be unlikely to change. Thealign text process 1406 may therefore set the block of cells bounded byrow 1, column 1 and row 3, column 3 to be immutable by locking thelinks. Additionally or alternatively, the locked cells may correspond tothose representing time prior to tf. Suppose further that the align textprocess 1406 determines that the presence of “x”s or absence (indicatedwith a “-”) in the locked block are in the correct locations and locksthem so the locked blocks do not subsequently change. The alignmentdecisions for the locked section may be sent to the vote process 1408and (contingent on approval by the vote process 1408) sent to a firstdevice as transcriptions. In some embodiments, a forward or backwardpass in locked cells may not be run because the results are not expectedto change. Further, supposing the cells in the path are correctly marked(e.g. with the “x” in cell row 3, column 3), then the search may berestricted to paths that include locked cells marked as part of thepath, simplifying the search beyond the locked cells. For example, ifthe “x” in the cell in row 3, column 3 is locked and the “-” is lockedin row 2, column 3, then paths potentially stemming from row 2, column 3may be ignored and potential paths stemming from row 3, column 3 may beevaluated. Thus, the number of potential paths to search may be reduced.Reduction of the number of paths to search may simply the search. As thesearch progresses from left to right, additional cells, “x” s, and “-”smay be locked, simplifying the search through cells subsequent to (e.g.,below and to the right of) the locked cells. A similar embodiment isdescribed below with reference to Matrix 1 and Matrix 2.

Additionally or alternatively, the align text process 1406 and votingprocess 1408 may be configured to fuse transcriptions in real time ornear real time by accumulating transcriptions from each transcriptiongeneration process 1402. The accumulated transcriptions, input to aligntext process 1406 and voting process 1408 as blocks of text, may then befused together to create an output hypothesis. Each time a new token orsequence of tokens is received from one of the transcription generationprocesses 1402, the new token or sequence of tokens may be appended tothe previously created input hypothesis to create an updated inputhypothesis. The updated input hypothesis may then be fused with otherhypotheses from other transcription generation processes 1402, and thefused output becomes the fused output hypothesis.

In some embodiments, to limit processing load or for other reasons, thealign text process 1406 and voting process 1408 may use a method of“windowing.” Windowing refers to creating a hypothesis by accumulatingthe output from speech transcribers, and when a hypothesis length isgreater than a selected window length (L), one or more tokens, startingfrom the beginning of the hypothesis, may be deleted until thehypothesis length is L tokens. This is similar to having a ‘window’ thatallows the align text process 1406 and voting process 1408 to see Ltokens of the hypothesis at a time. By deleting tokens in this manner,the hypothesis provided to the align text process 1406 and votingprocess 1408 may be kept at manageable lengths.

In some embodiments, one or more of the transcription generationprocesses 1402 may lag behind other of the transcription generationprocesses 1402. For example, a transcription generation process 1402with a relatively higher latency than other transcription generationprocesses 1402 may output a transcription that lags behind, in time, thetranscriptions of the other transcription generation processes 1402. Asa result, the window for each transcription generation process 1402 maycover a different segment of time. In these and other embodiments, thealign text process 1406 and voting process 1408 may be configured tooutput only words that are estimated to be within the window of all orsome number of the transcription generation processes 1402. For example,the align text process 1406 and voting process 1408 may be configured toonly output tokens that come after the pth token from the beginning of awindow and before the qth token from the end of the window. For example,if the window is fifty tokens (L=50), p=9, and q=5, the align textprocess 1406 and voting process 1408 may output tokens ten throughfourty-five.

In these and other embodiments, hypotheses, such as token sequences,provided to the align text process 1406 may be realigned each time newor corrected tokens or token sequences are received from thetranscription generation processes 1402. The realignment of the tokensequences may be performed for tokens within the windows.

Additionally or alternatively, the align text process 1406 may remembercontext from a previous alignment using a sliding window. In someembodiments, to remember context from a previous alignment using asliding window, the align text process 1406 may be configured to aligntoken sequences from the transcription generation processes 1402 bycreating a dynamic programming matrix. In these and other embodiments,the align text process 1406 may retain context with alignment by notcreating a new matrix each time new tokens or a sequence of tokens isreceived from the transcription generation processes 1402, but byretaining information in the matrix regarding the previous tokens orsequence of tokens received from the transcription generation processes1402. The example matrix below (Matrix 1) shows alignment between twosequences, seq1 and seq2, using an example where seq1=“A B C D” andseq2=“A C C D.” The values in the matrix show the cumulative Levenshteinedit distance as computed using, for example, the Viterbi or theWagner-Fischer method. The edit distance used in this example may countone point for an insertion, deletion, or substitution. Once matrixvalues are determined, the alignment may be determined from the paththat reduces the edit distance.

Matrix 1 seq1 word1 = A word2 = B word3 = C word4 = D seq2 word1 = A 0 12 3 word2 = C 1 1 1 2 word3 = C 2 2 1 2 word4 = D 3 3 2 1

The example matrix below (Matrix 2) shows an example where two moretokens are provided for each sequence and the first two tokens in eachof the sequences are set to ‘locked.’ In these and other embodiments,the locked tokens may then be appended to the fused transcription outputby the align text process 1406 and voting process 1408. Instead ofre-computing the entire dynamic programming table represented inmatrices, the cells corresponding to the locked token, such as (the tworows containing “word1=A” and “word2=C” and the two columns containing“word1=A” and “word2=B”) may be removed from the matrix 2. Calculationsfor the new elements of the matrix may be performed and the remainingportions of the table may then be used to align new words in thesequence. As a result, only 12 of 16 elements (the 4×4 grid of cells atthe bottom-right of Matrix 2, minus the four that were previouslycomputed) may be newly determined instead of calculating all 16elements.

Matrix 2 seq1 word1 = A word2 = B word3 = C word4 = D word5 word6 seq2word1 = A 0 1 2 3 word2 = C 1 1 1 2 word3 = C 2 2 1 2 word4 = D 3 3 2 1word5 word6

The sliding window method is illustrated in Matrix 2 above in twodimensions for the case of two input sequences, but a multi-dimensionalversion may be used with more than two input sequences.

In some embodiments, the align text process 1406 and voting process 1408may be configured to operate with a variable delay. In these and otherembodiments, the variable delay may be responsive to how many tokens ininput token sequences match. In these and other embodiments, the aligntext process 1406 and voting process 1408 may use shorter delays forinput sequences with more tokens that match.

For example, if input sequences from a transcription generation process1402 process each contain a series of one or more tokens that matchacross one or more other input sequences, the align text process 1406and voting process 1408 may output the series of tokens immediately. Ifone or more words are different across input sequence, the align textprocess 1406 and voting process 1408 may wait for more tokens beforedetermining and outputting a result. As another example, suppose, at acertain point in time, a first input sequence includes “meet you at thetrain station at four” and the second input sequence includes “meet youat the train.” The token sequence of “meet you at the train” matches. Asa result, the align text process 1406 and voting process 1408 may outputthe sequence of “meet you at the train” immediately or after some minordelay without waiting for another token. Suppose, however, the secondinput sequence ends with “meet you at the trade.” In this case, thealign text process 1406 and voting process 1408 may wait for subsequenttokens for the second input sequence after the token “trade” beforedeciding whether to output “train” or “trade.” Additionally oralternatively, the align text process 1406 and voting process 1408 mayoutput the sequence immediately or after some minor delay withoutwaiting for another token. After receiving a subsequent token, the aligntext process 1406 and voting process 1408 may determine if the sequenceincluded an error. If the sequence included an error, a correction ofthe error may be provided to the first device 104.

For purposes other than providing transcriptions during communicationsessions, such as generating transcriptions for training models andmeasuring accuracy, real-time or near real-time operations may not benecessary. In these and other embodiments, the align text process 1406and voting process 1408 may be configured to operate on larger blocks ofinput or even to wait for the end of a session (such as a communicationsession) or a speaker saying words in a conversation before fusingtranscriptions. In these and other embodiments, matching of inputsequences, evaluating matching of input sequences, and other processesdiscussed herein with respect to the process 1400 may be run lessfrequently than when providing transcriptions during a communicationsession in or at real-time.

Modifications, additions, or omissions may be made to FIG. 14 and/or thecomponents operating in FIG. 14 without departing from the scope of thepresent disclosure.

FIG. 15 illustrates an example environment 1500 for addingcapitalization and punctuation to a transcription, arranged according tosome embodiments of the present disclosure. In some embodiments, theenvironment 1500 may include a transcription unit 1514 that includes anASR system 1520. The ASR system may include a word recognizer 1502, apunctuator 1504, and a capitalizer 1515.

In some embodiments, the ASR system 1520 may be a revoiced ASR. In theseand other embodiments, the ASR system 1520 may obtain the revoicing ofaudio from a CA. In some embodiments, the CA may recite punctuation inthe revoicing of the audio. For example, the CA may say keywords such as“comma,” “period,” and “question mark,” in addition to the words of theaudio where the punctuation should be added in the audio. The wordrecognizer 1502 may be configured to recognize such punctuation keywordsand place the corresponding punctuation marks in the transcriptiongenerated by the ASR system 1520. In these and other embodiments, theASR system 1520 may be configured to ignore punctuation keywords suchthat the punctuation keywords voiced by the CA are not included in thetranscription as words, such as “comma”

In some embodiments, the CA may recite capitalization in the revoicingof the audio. For example, the CA may say a keyword such as“capitalize,” “capital,” or “all caps,” in addition to the words of theaudio to indicate the words or letters that should be capitalized. Theword recognizer 1502 may be configured to recognize such capitalizationkeywords and cause the corresponding words to be capitalized in thetranscription generated by the ASR system 1520. In these and otherembodiments, the ASR system 1520 may be configured to ignorecapitalization keywords such that the capitalization keywords voiced bythe CA are not included in the transcription as words, such as“capitalize.”

Additionally or alternatively, capitalization and punctuation may beautomatically generated by the ASR system 1520 using the punctuator 1504and the capitalizer 1515, as will be explained hereafter.

Capitalization and punctuation may be provided in transcriptions thatare provided to a user device for presentation. In some embodiments, atranscription may be sent with the incorrect capitalization andpunctuation or that lacks capitalization and punctuation. In these andother embodiments, corrected capitalization and punctuation for atranscription may be provided to the user device for presentation. Insome embodiments, the transcription unit 1514 may obtain the correctionsof capitalization and punctuation based on input from a CA or from otherprocesses performed by the ASR system 1520.

In some embodiments, the punctuator 1504 may be configured to use apunctuation model 1506 to punctuate a transcription generated by the ASRsystem 1520. In these and other embodiments, the ASR system 1520 maygenerate the transcription without punctuation as described previously.The punctuator 1504 may use the punctuation model 1506 to add thepunctuation to the transcription.

The punctuation model 1506 may be generated by a punctuation modeltrainer 1508. The punctuation model trainer 1508 may obtain text thatincludes the punctuation and text that does not include punctuation. Thetext may be stored by a punctuation database 1530 as a corpus ofpunctuated text. The text from the corpus may be provided by thepunctuation database 1530 to the punctuation model trainer 1508 and to apunctuation remover 1532. The punctuation remover 1532 may remove thepunctuation from the text and provide the text without the punctuationto the punctuation model trainer 1508.

The punctuation model trainer 1508 may include a first feature extractor1509 a and a second feature extractor 1509 b, referred to as the featureextractors 1509. The punctuated text may be provided to the firstfeature extractor 1509 a. The unpunctuated text may be provided to thesecond feature extractor 1509 b. The feature extractors 1509 may beconfigured to extract features from the text, such as n-grams. Thefeature extractors 1509 may provide the extracted features to a dataanalyzer 1510 a. In some embodiments, the data analyzer 1510 a may usemachine learning that does not use separate feature extraction, in whichcase, one or more feature extractors 1509 may be omitted.

A data analyzer 1510 a may use the extracted features and informationfrom a punctuated term list 1512, which may include a list ofabbreviations, acronyms, regular expressions, and other words or phrasesto be punctuated, to train a punctuation model 1506. The punctuationmodel 1506 may include rules to allow the ASR system 1520 to punctuatetranscriptions. The punctuation model 1506 may be provided to thepunctuator 1504. The punctuator 1504 may use the punctuation model 1506to punctuate transcriptions generated by the ASR system 1520.

Additionally or alternatively, the punctuator 1504 may use thepunctuation model 1506 to insert punctuation into a second corpus oftext, such as text generated from transcriptions generated by atranscription system that includes the transcription unit 1514. Thesecond corpus of text may be provided to the punctuation database 1530.In some embodiments, the first corpus and/or the second corpus may beused by the punctuation model trainer 1508 to generate a secondpunctuation model that may be used by the punctuator 1504. In thismanner, the punctuation model may be updated as the transcription unit1514 generates additional transcriptions.

In some embodiments, the punctuation model 1506 may also be trainedusing punctuation spoken by a CA or edits obtained from a CA. Forexample, if a CA revoices an audio sample and says “don't leave periodif you go comma i'll hang up period” then the punctuator may be trainedon the punctuated text “don't leave. if you go, i'll hang up.” Thistraining may occur on-the-fly or on recorded text.

Additionally or alternatively, the ASR system 1520 may add punctuationto a transcription using other methods. For example, one or more methodsmay be used as described below in Table 7.

TABLE 7 1. The audio stream is analyzed to identify silence segments andto determine a pitch contour over time. A set of rules assignspunctuation based on the duration of silence segments, time betweensilence gaps, and shape of the pitch contour. For example, a period maybe assigned when a silence segment greater than a particular period oftime (e.g. 0.5 seconds) is found, the time since the previous silencesegment is at least a specified period of time (e.g., three seconds),and the voice pitch frequency since the previous silence segment hasdropped by at least a minimum percentage (e.g., 10%). 2. The punctuator1504 uses a punctuation model 1506 to punctuate text as previouslydescribed. 3. A set of rules responsive to text input may be constructedfor adding punctuation to text. For example, the set of rules mayinclude a list of punctuated phrases, including regular expressions,where punctuation from the phrases is applied to text when the wordsfrom the text match words from the list of punctuated phrases. 4.Punctuation may be added using a combination of the above methods. Forexample a. A punctuation mark may be added when the set of rulesdescribed in method #1 above and the punctuator 1504 listed in method #2above both agree on a mark and its location. b. A model such as arecurrent or convolutional neural network may be trained on acoustic andtext features to generate punctuation.

In some embodiments, the capitalizer 1515 may be configured to use acapitalization model 1518 to punctuate a transcription generated by theASR system 1520. In these and other embodiments, the ASR system 1520 maygenerate the transcription without punctuation as described previously.The capitalizer 1515 may use the capitalization model 1518 to add thepunctuation to the transcription.

The capitalization model 1518 may be generated by a capitalization modeltrainer 1516. The capitalization model trainer 1516 may obtain text thatincludes capitalization and text that does not include capitalization.The text may be stored by a capitalization database 1522 as a corpus ofcapitalized text. The text from the corpus may be provided by thecapitalization database 1522 to the capitalization model trainer 1516and to a capitalization remover 1523. The capitalization remover 1523may remove the capitalization from the text and provide the text withoutthe capitalization, such that the text is all lower case, to thecapitalization model trainer 1516.

The capitalization model trainer 1516 may include a first featureextractor 1517 a and a second feature extractor 1517 b, referred to asthe features extractors 1517. The capitalized text may be provided tothe first feature extractor 1517 a. The un-capitalized text may beprovided to the second feature extractor 1517 b. The feature extractors1517 may be configured to extract features from the text, such asn-grams. The feature extractors 1517 may provide the extracted featuresto a data analyzer 1510 b. In some embodiments, types of machinelearning may be employed such that feature extraction may not be used.

The data analyzer 1510 b may use the extracted features and informationfrom a capitalized term list 1521, which may include a list of propernames, abbreviations, acronyms, regular expressions, and other terms tobe capitalized, to train the capitalization model 1518. Thecapitalization model 1518 may include rules to allow the ASR system 1520to capitalize transcriptions. The capitalization model 1518 may beprovided to the capitalizer 1515. The capitalizer 1515 may use thecapitalization model 1518 to capitalize transcriptions generated by theASR system 1520.

Additionally or alternatively, the capitalizer 1515 may use thecapitalization model 1518 to insert capitalization into a second corpusof text, such as text generated from transcriptions generated by atranscription system that includes the transcription unit 1514. Thesecond corpus of text may be provided to the capitalization database1522. In some embodiments, the first corpus and/or the second corpus maybe used by the capitalization model trainer 1516 to generate a secondcapitalization model that may be used by the capitalizer 1515. In thismanner, the capitalization model may be updated as the transcriptionunit 1514 generates additional transcriptions.

In some embodiments, the capitalization model 1518 may also be trainedusing capitalization spoken by a CA or edits of capitalization obtainedfrom a CA using, for example, a text editor.

In some embodiments, the ASR system 1520 may add capitalization to atranscription using other methods than the method described above withrespect to the capitalizer 1515. For example, one or more methods may beused as described below in Table 8.

TABLE 8 1. The audio stream is analyzed to identify silence segments andto determine a pitch contour over time. A set of rules assignscapitalization based on the duration of silence segments, time betweensilence gaps, and shape of the pitch contour. For example, when a firstsilence segment greater than a first time period (e.g., 0.5 seconds) isfound, the time since the previous silence segment is at least a secondtime period (e.g., three seconds), and the voice pitch frequency sincethe previous silence segment has dropped at least a specified percentage(e.g., 10%), the first letter following the first silence segment may becapitalized. 2. A capitalizer 1515 uses a capitalization model tocapitalize text and may be responsive to punctuation inserted by thepunctuator 1504. 3. A set of rules responsive to text input may beconstructed for adding capitals to text. For example, the first letterfollowing a period and the first letter of words that match entries in acapitalized term list may be capitalized. 4. Capitalization may be addedusing a combination of the above methods. For example, a. A letter maybe capitalized when either the set of rules described in method #1 aboveor the capitalizer described in method #2 above determine that theletter should be capitalized. b. A model such as a recurrentconvolutional neural network may be trained on acoustic and textfeatures to generate capitalization.

capitalizing and punctuating a transcription may incur some latency. Inthese and other embodiments, the latency may be due to the capitalizer1515 and punctuator 1504 using input ahead (i.e., in the future) of thepoint where capitalization and punctuation are inserted. In someembodiments, overall latency may be reduced by converting audio to textusing a first method that provides a lower accuracy transcription withlower latency and a second method that provides a higher accuracytranscription with higher latency. Capitalization and punctuationdetermined using the first method may be applied to a transcriptiondetermined using the second method to reduce the latency. Alternativelyor additionally, the capitalizer 1515 and the punctuator 1504 mayfurther determine capitalization and punctuation based on the secondtranscription. A comparison may be made between the first transcriptionand the second transcription to determine errors. Some or all of theerrors may be corrected and may be sent to the first device ascorrections.

In some embodiments, training the punctuation model 1506 and thecapitalization model 1518 may be performed using transcription generatedby the transcription unit 1514. In these and other embodiments, thetranscriptions may include personal information and non-personalinformation. The non-personal information may be stored and the personalinformation deleted using methods disclosed herein for storing data totrain language and acoustic models. For example, n-grams may beextracted from the transcriptions, filtered for privacy, and saved,together with capitalization and punctuation marks. In another example,transcriptions are filtered for privacy and stored (see FIG. 60). Inthese and other embodiments, the capitalization model 1518 and thepunctuation model 1506 may be built from the saved data and features.Methods for extracting n-grams and applying privacy filters aredescribed below in greater detail.

Modifications, additions, or omissions may be made to the environment1500 without departing from the scope of the present disclosure. Forexample, the punctuation model trainer 1508 and the capitalization modeltrainer 1516 may not include the punctuation model trainer 1508 and thecapitalization model trainer 1516 as described. Rather, the punctuationmodel trainer 1508 and the capitalization model trainer 1516 may includeneural networks that may be built or adapted on-the-fly, using forexample, gradient descent training and machine learning to generate thepunctuation model 1506 and the capitalization model 1518. Additionaldetails regarding training models on-the-fly are disclosed in thecontext of training acoustic and language models with reference to FIGS.74 and 84.

FIG. 16 illustrates an example environment 1600 for providingcapitalization and punctuation to fused transcriptions, arranged inaccordance with some embodiments of the present disclosure. Theenvironment 1600 may include a first ASR system 1620 a, a second ASRsystem 1620 b, and a third ASR system 1620 c, collectively referred toas the ASR system(s) 1620. The environment 1600 may also include a firstpunctuation converter 1624 a, a second punctuation converter 1624 b, anda third punctuation converter 1624 c, collectively referred to as thepunctuation converter(s) 1624. The ASR systems 1620 may obtain audio andgenerate transcriptions of the audio. The ASR systems 1620 may providethe transcriptions to the punctuation converters 1624. The punctuationconverters 1624 may be configured to remove punctuation or convertpunctuation from the transcriptions to a format suitable for fusion.

The environment 1600 may also include a first capitalization converter1625 a, a second capitalization converter 1625 b, and a thirdcapitalization converter 1625 c, collectively referred to as thecapitalization converter(s) 1625. The capitalization converters 1625 maybe configured to remove capitalization or convert capitalization of thetranscriptions from the ASR systems 1620 to a format suitable forfusion. The environment 1600 may also include a fuser 1622, acapitalizer 1615, and a punctuator 1604. In some embodiments, thepunctuator 1604 may be configured to replace punctuation after fusion oftranscriptions by the fuser 1622. Alternatively or additionally, thecapitalizer 1615 may be configured to replace capitalization afterfusion.

The environment 1600 may operate in one or more different modes. Thedifferent modes are discussed in turn.

In a first mode, the punctuation and capitalization of thetranscriptions generated by the ASR systems 1620 may be converted intotags by the punctuation converters 1624 and capitalization converters1625. The tags may be inserted into the transcriptions. Thetranscriptions may be provided to the fuser 1622. The fuser 1622 mayfuse the transcriptions with the tags. For example, the phrase “Jacob issick.” may be converted to “Jacob is sick period_” by the punctuationconverter 1624. The capitalization converter 1625 may convert “Jacob issick _period_” to “_capital_ jacob is sick period_”. In someembodiments, the punctuation converters 1624 may separate punctuationmarks from each word by at least one space or using whitespace so thatthe fuser 1622 processes words and punctuation marks as separate tokens.

After fusion of the transcriptions with the inserted tags by the fuser1622, the tags of the fused transcription may be converted back topunctuation and capitalization. In some embodiments, the fuser 1622 maybe configured to treat tags as regular tokens for purposes of alignmentand voting. Additionally or alternatively, the fuser 1622 may beconfigured to ignore tags or may assign weights for tags that aredifferent from weights for other tokens for purposes of alignment and/orvoting. In these and other embodiments, the capitalizer 1615 and thepunctuator 1604 may not be utilized.

In a second mode, a first transcription, which contains marks such aspunctuation and capitalization, is generated by the first ASR system1620 a. The first transcription is provided to the fuser 1622 withouthaving the punctuation and capitalization removed. The firsttranscription may be selected to not have the punctuation andcapitalization removed based on the first transcription having thehighest word and/or capitalization and/or punctuation accuracy.

In these and other embodiments, the second and third punctuationconverters 1624 b and 1624 c may be configured to remove the punctuationfrom the transcriptions from the second and third ASR systems 1620 b and1620 c. The second and third capitalization converters 1625 b and 1625 cmay be configured to remove the capitalization from the transcriptionsfrom the second and third ASR systems 1620 b and 1620 c. Thus, thetranscriptions from the second and third ASR systems 1620 b and 1620 cmay not include punctuation and capitalization and may be provided tothe fuser 1622.

The fuser 1622 may be configured to pass through all punctuation andcapitalization and to keep punctuation aligned in the transcriptions. Inthese and other embodiments, the fuser 1622 may combine thetranscriptions from the ASR systems 1620 into a fused transcription. Thefuser 1622 may also be configured to align the fused transcription withthe first transcription to insert the capitalization and the punctuationfrom the first transcription into the fused transcription. In these andother embodiments, multiple transcriptions may be fused with punctuationand capitalization. Alternatively or additionally, multipletranscriptions may be fused without punctuation and capitalization. Thetwo groups of fused transcriptions may be fused to add punctuation andcapitalization.

In a third mode, punctuation and capitalization are removed from thetranscriptions before fusion by the fuser 1622. In these and otherembodiments, the punctuation converters 1624 and the capitalizationconverters 1625 may be configured to remove the punctuation andcapitalization. A record of the locations of the punctuation andcapitalization may be retained and provided to the capitalizer 1615 andthe punctuator 1604. In these and other embodiments, the capitalizer1615 and the punctuator 1604 may receive the record of the locations ofthe punctuation and capitalization and may be configured to reinsert thepunctuation and capitalization into the transcriptions fused by thefuser 1622.

In a fourth mode, punctuation and capitalization may be attached asattributes to tokens in the transcriptions by the punctuation converters1624 and the capitalization converters 1625, respectively. Theattributes and the tokens in the transcriptions may be provided to thefuser 1622. The fuser 1622 may align the tokens and may select tokensand attributes of punctuation and capitalization based on a votingprocess as described previously. For example, if three hypothesesinclude:

-   -   “Go, please.”    -   “No. Please.”    -   “So please.”        and “go please” is selected by voting, then the attributes of        “go” (an initial capital and a coma) may be retained since “go”        was selected through voting. Alternatively or additionally, the        word “please” may be rendered with a lower-case “p,” because the        lower-case version of the word has a two-to-one majority, and        the period after “please” may be retained since it is        unanimously attached to “please.” The fused result may then be        “Go, please.” in this example. The same method may be used to        attach other attributes such as hyphens, words in all capitals,        mixed capitals such as camelCase, contractions, apostrophes,        accents, diacritics, etc., to tokens. Based on a token being        selected, the attributes attached to words may be selected or a        further process to vote on the attribute may be performed.

As described, various embodiments disclose methods for providing bothpunctuation and capitalization. However, the providing of punctuationand capitalization may be separated. In these and other embodiments, thesteps for providing punctuation may be implemented without providingcapitalization and the steps for providing capitalization may beimplemented without providing punctuation.

Modifications, additions, or omissions may be made to the environment1600 without departing from the scope of the present disclosure. Forexample, in some embodiments, the punctuator 1604, capitalizer 1615, thepunctuation converters 1624, and capitalization converters 1625 may notbe part of the environment 1600.

As another example, the environment 1600 may not include one or more ofthe punctuation converters 1624 and capitalization converters 1625 andone or more of the punctuator 1604 and the capitalizer 1615.

As another example, the environment 1600 may not include the punctuationconverters 1624, and capitalization converters 1625. In these and otherembodiments, the ASR systems 1620 may generate transcriptions withoutpunctuation or capitalization and send the transcriptions to the fuser1622. The fuser 1622 may fuse the transcriptions to generate a fusedtranscription. The fused transcription may be provided to thecapitalizer 1615 and punctuator 1604 to add capitalization andpunctuation, respectively, to the fused transcription.

FIG. 17 illustrates an example environment 1700 for transcription ofcommunications, in accordance with some embodiments of the presentdisclosure. The environment 1700 may include a device 1704, atranscription system 1708, and an enhanced transcription generator 1702.The device 1704 may be associated with a user and may be configured toobtain and provide audio to the transcription system 1708 and theenhanced transcription generator 1702. The transcription system 1708 maybe configured to generate transcriptions of the audio and provide thetranscriptions to the device 1704 and the enhanced transcriptiongenerator 1702. The device 1704 may include a display 1705, upon whichthe transcription of the audio may be presented.

In some embodiments, a user that is presented the transcription by thedevice 1704 may struggle to decipher emotion and word emphasis from theplain text of the transcription. In particular, the user may struggle todecipher emotion and word emphasis from plain text if the user's hearingloss is significant and the user relies more on transcriptions thanaudio to carry on the conversation. In these and other embodiments, ifthe user is unable to detect attributes such as subtle changes in aspeaker's tone of voice, he or she may miss important cues which canlead to misunderstandings. In these and other embodiments, the enhancedtranscription generator 1702 may be configured to obtain the audio andprovide instructions to the device 1704 regarding adjusting thepresentation of the transcription such that the presented transcriptionprovides context regarding emotion and word emphasis from the audio. Thepresentation of the transcription may be adjusted by adjusting thepresentation of certain words in the transcription or adding symbols tothe transcription.

In some embodiments, a user who may rely on the presented transcriptionsto understand the conversation may also struggle to detect keyinformation in a presented transcription. In these and otherembodiments, the enhanced transcription generator 1702 may also beconfigured to obtain the audio and provide instructions to the device1704 regarding adjusting the presentation of the transcription such thatthe presentation of key words/phrases (for example: names, appointmenttimes, phone numbers, and locations) in the transcription are adjustedto distinguish the key words/phrases from other portions of thetranscription.

In some embodiments, the enhanced transcription generator 1702 may beconfigured to analyze the audio of the speaker and/or the transcriptionsgenerated by the transcription system 1708 to identify emotion, wordemphasis, key words, and/or phrases, among other aspects of aconversation. In some embodiments, to identify emotion, word emphasis,key words, and/or phrases, the enhanced transcription generator 1702 mayinclude one or more of: a text analyzer 1710, a pitch analyzer 1712, anenergy detector 1714, a spectrum analyzer 1716, or a waveform analyzer1718. In these and other embodiments, one or more of the text analyzer1710, pitch analyzer 1712, energy detector 1714, spectrum analyzer 1716,and waveform analyzer 1718 may be configured to obtain and analyze theaudio. One or more of the text analyzer 1710, pitch analyzer 1712,energy detector 1714, spectrum analyzer 1716, and waveform analyzer 1718may provide an analysis of the audio to a detector 1720. The analysis ofthe audio may determine values of or changes in pitch, volume, speakingrate, features derived from spectral characteristics, and other factors.The detector 1720 may be configured to compare the values or changes toproperty characteristics of the aforementioned attributes to determineemotion, word emphasis, key words, and/or phrases. The detector 1720 mayalso be configured to associate the determined emotion and word emphasiswith associated words in the transcription. The detector 1720 mayprovide an indication of the words in the transcription that may beadjusted, the type of adjustment, and/or symbols, such as words,characters, or other symbols that may be added to the transcription toattempt to convey the determined emotion and word emphasis.

In some embodiments, the detector 1720 may also be configured to applynatural language processing or other techniques to the transcription toassist in identifying key words, and/or phrases. The detector 1720 mayprovide an indication of the identified key words and/or phrases in thetranscription that may be adjusted and the type of adjustment.

In some embodiments, the detector 1720 may be configured to mark thewords or phrases in the transcription that may be adjusted. In these andother embodiments, the words or phrases in the transcription may bemarked with tags such as XML tags (similar to the <c> and </c> tags thatmay be used to denote corrections). Marking the words may adjust apresentation of the words. The adjustments to the words may include oneor more of the following, among others:

-   -   1. Changing the font (e.g., Helvetica vs. Courier)    -   2. Changing the font color    -   3. Bolding    -   4. Italicizing    -   5. Underlining    -   6. Highlighting    -   7. Graphics or images near the text such as an arrow or pointing        finger    -   8. Graphics surrounding the text such as a box or other        enclosure    -   9. An effect that changes over time such as sparkles, pulsing        text, text that vibrates or is otherwise in motion, a video, or        a strobe effect    -   10. Capitalization (such as all-caps)    -   11. Inserting an emoji or emoticon    -   12. Inserting text descriptions such as “(angry)” or        “[laughing]”    -   13. Delimiting, for example, emphasized text with characters        such as braces or other markings    -   14. A combination of the above.

In some embodiments, the detector 1720 may provide the transcriptionwith the marked words and/or additional symbols to the device 1704. Thedevice 1704 may present the transcription on the display 1705. Thedevice 1704 may present the adjustments to the transcription based onthe tags in the transcription. As illustrated in FIG. 17, various wordsmay be adjusted to denote emotion. For example, the words, “wreck” and“lake” are bolded. Additionally, an emoticon is added to convey theemotion of the speaker.

Additionally or alternatively, when words are presented on the display1705 that may be associated with data for which other aspects of thedevice 1704 may use to perform functions, such as telephone numbers,email addresses, references to additional information, URLs or otherInternet addresses, etc., links to allow usage of the data may becreated. For example, the display 1705 may be a touch screen that allowsa user to click a link or an icon such as the consent input element 1709or the settings 1707 to activate. The words associated with the data,for which links may be created, may be displayed in a manner thatindicates the word is associated with the link. For example, a word maybe displayed with a color change or with underlining to indicate thatthe word is associated with a link. For example, the words “Bob's Deli”are underlined in FIG. 17. The user may interact with the device 1704 toactivate the link. Activating the link may include dialing a phonenumber, sending email, displaying additional information, or visiting aweb page.

In some embodiments, the device 1704 may be configured to present a userinterface that may obtain input from the user regarding settings 1707that may be used to adjust the transcriptions. For example, the settings1707 may allow for control of turning on or off each type of adjustment,such as emotion, word emphasis, and key words/phrases, individually ortogether as a group. Additionally or alternatively, when thetranscription system 1708 sends a transcription, and later an update tothe transcription, such as a correction to the transcription, the updatemay show as a strikethrough through the incorrect word with an emphasison the corrected word. Alternatively or additionally, an update from thetranscription system 1708 may cause the device 1704 to highlight thecorrection. In some embodiments, as will be discussed below in greaterdetail with reference to FIG. 53, the device 1704 may also be configuredto obtain user input with respect to consent from the user to record aconversation through a consent input element 1709.

Modifications, additions, or omissions may be made to the environment1700 without departing from the scope of the present disclosure. Forexample, in some embodiments, the enhanced transcription generator 1702is illustrated as being separate from the device 1704 and thetranscription system 1708. In some embodiments, the enhancedtranscription generator 1702 may be part of the device 1704 or thetranscription system 1708. As another example, the display 1705 may bepart of another device, such as a television that communicates with thedevice 1704.

FIGS. 18-30, among others, describe various systems and methods that maybe used to select between different transcription units for providingtranscriptions for obtained audio. Alternatively or additionally, FIGS.18-30, among others, describe various systems and methods that mayswitch between the different transcription units providingtranscriptions for audio of a communication session during thecommunication session. In these and other embodiments, a criteria forselecting between transcription units may include the estimated accuracyof each transcription unit. For example, when a non-revoicingtranscription unit provides an estimated accuracy that satisfies athreshold, the non-revoicing transcription unit may be selected over arevoicing transcription unit.

FIG. 18 illustrates another example environment 1800 for transcriptionof communications, in accordance with some embodiments of the presentdisclosure. The environment may include a first switch 1804 a, a secondswitch 1804 b, a third switch 1804 c, referred to collectively as theswitches 1804, a selector 1806, a first transcription unit 1814 a, and asecond transcription unit 1814 b.

The first transcription unit 1814 a may be a revoiced transcription unitas previously described. The second transcription unit 1814 b may be anon-revoiced transcription unit as previously described. The switches1804 may be configured to direct or pass data, such as audio andtranscriptions based on their configuration. The configuration of theswitches may be controlled by the selector 1806.

In some embodiments, the first switch 1804 a and the second switch 1804b may be configured to obtain audio. The audio may be from acommunication session or from some other source. The first switch 1804 amay be configured to block the audio or pass the audio to the firsttranscription unit 1814 a based on the configuration of the first switch1804 a. The first transcription unit 1814 a may generate a transcriptionof the audio and provide the transcription to the third switch 1804 c.The second switch 1804 b may be configured to block the audio or passthe audio to the second transcription unit 1814 b. The secondtranscription unit 1814 b may be configured to generate a transcriptionof the audio and provide the transcription to the third switch 1804 c.

In some embodiments, the third switch 1804 c may select between atranscription from the first transcription unit 1814 a or the secondtranscription unit 1814 b. The selector 1806 may control the switches1804. Thus, the selector 1806 may be configured to determine when audiois sent to the first transcription unit 1814 a and the secondtranscription unit 1814 b and which transcription is output by the thirdswitch 1804 c. The selector 1806 may be configured to control theswitches 1804 independently. For example, the selector 1806 may directthe first switch 1804 a to direct audio to the first transcription unit1814 a and also direct the second switch 1804 b to direct audio to thesecond transcription unit 1814 b, in overlapping time periods. In theseand other embodiments, both the first transcription unit 1814 a and thesecond transcription unit 1814 b receive the same audio at approximatelythe same or at the same time. In these and other embodiments, both thefirst transcription unit 1814 a and the second transcription unit 1814 bmay generate transcriptions and/or other data.

As another example of independent control of the switches 1804 by theselector 1806, when switching audio away from the first transcriptionunit 1814 a, the second switch 1804 b may direct audio to the secondtranscription unit 1814 b before the first switch 1804 a stops providingthe audio to the first transcription unit 1814 a so that the secondtranscription unit 1814 b may begin generating transcriptions before thefirst transcription unit 1814 a stops generating transcriptions.Likewise, when switching from the second transcription unit 1814 b tothe first transcription unit 1814 a, the first switch 1804 a may sendaudio to the first transcription unit 1814 a for a period of time beforethe second transcription unit 1814 b stops generating transcriptions,allowing the first transcription unit 1814 a to begin generatingtranscriptions.

As another example of independent control of the switches 1804 by theselector 1806, when the third switch 1804 c switches betweentranscriptions from the first transcription unit 1814 a and the secondtranscription unit 1814 b, the third switch 1804 c may be timed toaccount for relative latency of each of the first transcription unit1814 a and second transcription unit 1814 b. For example, if the latencythrough the first transcription unit 1814 a is x (four, for example)seconds and the latency through the second transcription unit 1814 b isy (one second, for example), when switching between the transcriptionfrom the first transcription unit 1814 a to the transcription from thesecond transcription unit 1814 b, the third switch 1804 c may wait (asdirected by the selector 1806) for a time period x-y (e.g., threeseconds) after the first switch 1804 a has directed audio to the secondtranscription unit 1814 b before selecting the transcriptions from thesecond transcription unit 1814 b. To avoid missing text, the selector1806 may direct the second switch 1804 b to send audio to the secondtranscription unit 1814 b before directing the third switch 1804 c toselect text from the second transcription unit 1814 b. Providing audioto the second transcription unit 1814 b in advance may also increaseaccuracy of the second transcription unit 1814 b by providing priorcontext to the language model. Similarly, the selector 1806 may directthe first switch 1804 a to send audio to the first transcription unit1814 a before directing the third switch 1804 c to select text from thefirst transcription unit 1814 a.

In some embodiments, the selector 1806, as discussed above, isconfigured to transmit control commands to the switches 1804 thatdetermine a destination of the audio and to select betweentranscriptions. In these and other embodiments, the selector 1806 may beconfigured to control the switches 1804 based on one or more featuressuch as accuracy of the second transcription unit 1814 b, availabilityof the first transcription unit 1814 a, and other features from Table 2and Table 5. Alternatively, both the selector 1806 and switches 1804 maybe implemented as software executed by a processor and configured todirect audio to different locations/destinations. The selector 1806 mayuse one or more of the following methods to control the switches 1804:

-   -   1. Start or continue a communication session with the second        transcription unit 1814 b. Add or switch to the first        transcription unit 1814 a if the estimated error rate of the        second transcription unit 1814 b exceeds a selected threshold.    -   2. Start or continue a communication session with the first        transcription unit 1814 a and run the second transcription unit        1814 b in parallel. When agreement between the transcriptions of        the first transcription unit 1814 a and the second transcription        unit 1814 b exceeds a selected threshold, switch to the second        transcription unit 1814 b.    -   3. Start or continue a communication session with the second        transcription unit 1814 b generating transcriptions and a CA        using a text editor to listen to communication session audio and        correct errors of the second transcription unit 1814 b. If a CA        correction rate falls below a selected threshold or if an        estimated error rate of the second transcription unit 1814 b        falls below a selected threshold, drop the CA.    -   4. Switch to the first transcription unit 1814 a if a new        speaker is detected.    -   5. Before transcription begins, use information on a        communication session characteristics (see Table 2) and        information on previous communication sessions with one or more        of the parties to make a decision to start the communication        session with the second transcription unit 1814 b, the first        transcription unit 1814 a, or a combination thereof (see Table        1).    -   6. Fuse the transcriptions from the second transcription unit        1814 b and the first transcription unit 1814 a to increase        accuracy

Modifications, additions, or omissions may be made to the environment1800 without departing from the scope of the present disclosure. Forexample, in some embodiments, the second transcription unit 1814 b maybe part of a transcription unit. Alternatively or additionally, thefirst switch 1804 a and the second switch 1804 b may be combined in asingle switch. Thus, while the switches 1804 are represented asdifferent devices, the switches 1804 may be included in any combinationof devices. Alternatively or additionally, the switches 1804 asdescribed may be implemented by hardware, software, or some combinationthereof that is configured to perform the functionality of the switches1804 as described in this disclosure.

Alternatively or additionally, the configurations and actions of theswitches 1804 as illustrated in FIG. 18 and with respect to otherFigures are illustrative and meant to convey actions of routing signalsand starting and stopping processes. Comparable actions may beimplemented by systems and/or methods. For example, instead of the firstand second switches 1804 a and 1804 b, the functionality of the firsttranscription unit 1814 a and the second transcription unit 1814 b maybe controlled. For example, the second transcription unit 1814 b mayreceive a signal to start or stop transcription of audio which mayresult in the same result as controlling the outputs of the secondswitch 1804 b. In another example, the action of a switch (a) directingaudio to or (b) blocking audio from a transcription unit may be replacedby sending audio to the transcription unit in either case and (a)selecting audio or (b) ignoring audio from the transcription unit.

The locations of switches 1804, the selector 1806, and the secondtranscription unit 1814 b are also illustrative. One or more of thosecomponents may alternatively be implemented by a processor executinginstructions on a device participating in a communication session fromwhich the audio discussed in this embodiment is obtained. For example,if the second transcription unit 1814 b executes on a device, theselector 1806 may send a signal to the device indicating whether audioshould be provided to the ASR system 1821 or to the first transcriptionunit 1814 a and from where transcriptions should be received, such asfrom the ASR system 1821 or the first transcription unit 1814 a. Inanother example, if the ASR system 1821 and the selector 1806 areimplemented on a device, the selector 1806 may determine that audio maybe processed to create transcriptions internally on the device by theASR system 1821, sent to an external location for processing with anoutside ASR system, or sent to the first transcription unit 1814 a forgenerating transcriptions.

FIG. 19 illustrates another example environment 1900 for transcriptionof communications, in accordance with some embodiments of the presentdisclosure. The environment 1900 may include a synchronizer 1902, afirst transcription unit 1914 a, and a second transcription unit 1914 b,collectively the transcription units 1914. The first transcription unit1914 a may be a revoiced transcription unit. The second transcriptionunit 1914 b may be a non-revoiced transcription unit. Each of thetranscription units 1914 may be configured to generate transcriptionsfrom audio and provide the transcriptions to the synchronizer 1902. Thefirst transcription unit 1914 a may generate a first transcription andthe second transcription unit 1914 b may generate a secondtranscription.

The synchronizer 1902 may be configured to obtain the transcriptionsfrom the transcription units 1914. The synchronizer 1902 may beconfigured to output one of the transcriptions. For example, thesynchronizer 1902 may output one of the transcriptions to a device forpresentation to a user.

The synchronizer 1902 may also be configured to change which one of thetranscriptions is being output. In these and other embodiments, thesynchronizer 1902 may be configured to transition between transcriptionsbeing output in a manner such that the output transcription does notinclude duplicate words from the first and second transcriptions, doesnot miss words that are included in the first and second transcriptions,and does not present words in an improper order. In short, thesynchronizer 1902 may be configured to switch between the first andsecond transcriptions used to provide the output transcription withoutthe output transcription including an indication of the switch betweenthe first and second transcriptions.

In some embodiments, to transition between the first and secondtranscriptions, the synchronizer 1902 may be configured to align thefirst and second transcriptions and check to ensure that thetranscriptions are in sync before making the switch. In someembodiments, the synchronizer 1902, may align the first and secondtranscriptions to compensate for differences in latency (i.e., the timedifference between audio input and text output) for the transcriptionunits 1914. In these and other embodiments, to align the first andsecond transcriptions, the synchronizer 1902 may add a constant delay tothe first and second transcriptions. Alternatively or additionally, thesynchronizer 1902 may wait for a silent segment or period of time withno speech such that neither of the first and second transcriptionsinclude text to switch between the first and second transcriptions.

A more specific example to transition between the transcriptions is nowprovided. The synchronizer 1902 may receive a first transcription T1from the revoiced first transcription unit 1914 a. The synchronizer 1902may receive a second transcription T2 from the non-revoiced secondtranscription unit 1914 b. The synchronizer 1902 may search for a firstsegment or sequence of words in the first transcription that aligns witha second segment in the second transcription. For example, suppose thefirst transcription and the second transcription at a given moment(e.g., within a sliding time window of a particular duration) end withthe following:

T2“ . . . going to stop worry about what you think I hope that doesn'tupset you or cause”

T1“ . . . finally decided I'm going to stop worrying what you think Ihope that”

Note that the speaker in this example is still speaking, so thesentences may not be complete. Note also that latencies of thetranscription unit 1914 may be different, so one of the transcriptions(in this example the second transcription T2) may contain more recenttext at a given point than the other. These transcriptions each containa segment that partly matches with the other (showing an alignedformat):

ASR: going to stop worry about what you think. I hope that CA: going tostop worrying — what you think. I hope that

The synchronizer 1902 may be configured to find segments in the firstand second transcriptions that match to within a selected set ofcriteria. The criteria may include, for example, a first rule that aminimum number of words match and a second rule regarding a maximumnumber of differences, for example that there are at least nine matchingwords and no more than two differences. Alternatively or additionally,the set of criteria may include a first rule that at least x (e.g.,five) words must be matched, a second rule that the number of matcherrors cannot exceed y % (e.g., 25%) of the words in the segment, and athird rule that the last word in both segments must be the same.

When the alignment between the first transcription and the secondtranscription satisfies the alignment criteria, the synchronizer 1902may output the aligned text and then switch between the firsttranscription and the second transcription. For example, thesynchronizer 1902 may output the first transcription up through the endof the aligned segment of the first transcription. After outputting theend of the aligned segment of the first transcription, the synchronizer1902 may output the second transcription beginning with the portion thatimmediately follows the aligned segment of the second transcription.

In some embodiments, the synchronizer 1902 may use other methods toalign or match the transcriptions. For example, the synchronizer 1902may use a Viterbi search or other dynamic programming method to alignand identify segment matches in the first and second transcriptions. Insome embodiments, the synchronizer 1902 may use information from thetranscription units 1914 to align the first and second transcriptions.For example, the synchronizer 1902 may use word endpoints from ASRsystems in the transcription units 1914 to align the first and secondtranscriptions. Alternatively or additionally, methods discussed withrespect to aligning text with respect to fusing of transcriptions mayalso be used to switch between the first transcription and the secondtranscription.

In some embodiments, in response to switching between transcriptions,the synchronizer 1902 may be configured to send a message to thetranscription units 1914 indicating the switch. As a result, theunselected transcription unit 1914 may be available to generatetranscriptions for other audio.

In some embodiments, the synchronizer 1902 may be configured, when orprior to switching from the non-revoiced second transcription unit 1914b to the revoiced first transcription unit 1914 a, to direct a CA clientof the revoiced first transcription unit 1914 a to display the secondtranscription or a summary of the second transcription over a precedingperiod of time. Displaying the second transcription may provide the CAperforming the revoicing for the revoiced first transcription unit 1914a context for the communication session. In these and other embodiments,when displaying the second transcription to the CA before and/or afterthe switch to the first transcription, the second transcription text maybe provided from a buffer. For example, the non-revoiced secondtranscription unit 1914 b may generate a lattice in response to audioand, when a switch between transcriptions occur, the lattice may bedecoded into text for display to the CA. Additionally or alternatively,audio may be saved, then, when the switch between transcriptions occur,the audio may be converted to text for display to the CA.

Additionally or alternatively, the CA client may direct the presentationof the second transcription over a longer period of time, during whichthe CA may provide edits to the second transcription. In these and otherembodiments, the CA client may receive an indication from a CA to directthe synchronizer 1902 to switch between the first and secondtranscription. Additionally or alternatively, audio may be broadcast toa CA so that the CA may listen to the previous portion of thecommunication session before the synchronizer 1902 switches to the firsttranscription from the second transcription.

Modifications, additions, or omissions may be made to FIG. 19 and/or thecomponents operating in FIG. 19 without departing from the scope of thepresent disclosure. For example, the environment 1900 may include one ormore switches or selectors as described with respect to FIG. 18.

FIG. 20 illustrates another example environment 2000 for transcriptionof communications, in accordance with some embodiments of the presentdisclosure. The environment 2000 may include a synchronizer 2002, afirst transcription unit 2014 a, and a second transcription unit 2014 b,collectively the transcription units 2014. The first transcription unit2014 a may be a revoiced transcription unit. The second transcriptionunit 2014 b may be a non-revoiced transcription unit. Each of thetranscription units 2014 may be configured to generate transcriptionsfrom audio and provide the transcriptions to the synchronizer 2002. Thefirst transcription unit 2014 a may generate a first transcription andthe second transcription unit 2014 b may generate a secondtranscription. The synchronizer 2002 may select one of the first andsecond transcriptions to output based on commands from the selector2006.

The environment 2000 may also include a switch 2004, a selector 2006,and a scorer 2016. The switch 2004 may be configured to be controlled bya selector 2006 to direct audio to the first transcription unit 2014 aor not direct audio to the first transcription unit 2014 a. In someembodiments, the selector 2006 may receive input from the secondtranscription unit 2014 b, the scorer 2016, and/or other automationdecision features to determine how to control the switch 2004 and thesynchronizer 2002. In these and other embodiments, the scorer 2016 mayprovide an indication of an agreement rate between the firsttranscription and the second transcription. Various examples of a scorer2016 are discussed in FIGS. 22 and 23.

In some embodiments, the selector 2006 may be configured to use theagreement rate from the scorer 2016, when the agreement rate isavailable, to determine how to control the switch 2004 and thesynchronizer 2002. In these and other embodiments, when the agreementrate is not available, such as when the first transcription unit 2014 ais not generating transcriptions, the selector 2006 may rely on otherfeatures to determine control decisions. In these and other embodiments,reference to making control decisions may relate to determining how tocontrol the switch 2004 and the synchronizer 2002, including whether theswitch 2004 may send audio to the first transcription unit 2014 a, whichof the first and second transcriptions the synchronizer 2002 may output,and whether the second transcription unit 2014 b may generatetranscriptions, among other control decisions regarding selectingbetween transcription units to generate transcriptions and selectingbetween transcriptions to output as discussed in this disclosure.

In some embodiments, the selector 2006 may also use as input todetermine control decisions the agreement rate, an estimated accuracy ofthe second transcription from the second transcription unit 2014 b, andother automation decision features. Alternatively or additionally, theselector 2006 may use only the estimated accuracy of the secondtranscription to determine control decisions. Alternatively oradditionally, the selector 2006 may use other performance measures fromthe second transcription unit 2014 b, such as average word confidence,sentence or phrase confidence, and likelihood ratio with respect to thesecond transcription, or other statistics or features from Table 2 orTable 5 to determine control decisions. In these and other embodiments,a likelihood ratio may be determined by subtracting the log likelihoodscores for the top two hypotheses in an n-best list from one or more ASRsystems of the second transcription unit 2014 b.

As another example, a combination of features may be derived frominternal ASR parameters from one or more ASR systems of the secondtranscription unit 2014 b and used to estimate accuracy or another ASRperformance measure. Examples of internal ASR parameters include, butare not limited to, the number of active arcs in a decoder search or theentropy or another statistic derived from the output probabilities froma neural network used as an acoustic model. In these and otherembodiments, an ASR performance measure may pertain to each word, anaverage over a phrase or speaking turn in a conversation, or an entiresession or conversation. In some embodiments, when using an ASRperformance metric to determine control decisions, the selector 2006 maybe configured to compare an ASR performance metric to a threshold. Inresponse to the ASR performance metric satisfying the threshold, theselector 2006 may determine control decisions.

Alternatively or additionally, the selector 2006 may further rely onfeatures unrelated to an ASR performance metric or an agreement rate todetermine control decisions. In these and other embodiments, thefeatures may include signal-to-noise ratio of the audio, speakercharacteristics of the participants in the communication sessiongenerating the audio, such as accent, and transcription complexity,among other features.

In some embodiments, the selector 2006 may determine control decisionson other data including a communication session history from previouscommunication sessions of the transcription party or other features fromTable 2 and Table 5. In these and other embodiments, based on acommunication session history, an initial control decision, such asselecting between the transcription units 2014 may be determined beforetranscriptions are generated. For example, the communication sessionhistory may include information including performance criteria, such asASR performance metrics from the second transcription unit 2014 b, thatmay be used to determine control decisions before transcriptions aregenerated.

In some embodiments, the selector 2006 may further rely on features suchas an account type (see Table 10 for examples), availability of thefirst transcription unit 2014 a, communication session priority, andother features from Table 2 to determine control decisions beforetranscriptions are generated.

An example operation of the environment 2000 is now provided. Theexample operation may pertain to the selector 2006 selecting one of thetranscription units 2014 based on previous communication sessioninformation. To begin, a connection between two or more users (a “firstuser” or “first party” and a “second user” or “transcription party”) ona first communication session may occur. Transcription may be generatedfrom the audio from the transcription party and provided to the firstparty. Information may be collected during the first communicationsession such as, ASR performance metrics, agreement rate, or otherfeatures from Table 2 or Table 5.

The collected information may be saved in a communication sessionhistory database. Two or more parties may be connected on a secondcommunication session. It may be determined if one or more of theparties has previously participated in a communication session. Inresponse to one or more of the parties having previously participated,information from the previous communication session may be analyzed.Based on collected information from the communication session historydatabase and the analysis of the collected information, the selector2006 may determine to use either one or both of the transcription units2014 (see Table 1) to provide transcriptions for the communicationsession. In some embodiments, the selection may be further based oninformation known about the second communication session before thesecond communication session begins. In some embodiments, the selectionmay be further based on features from Table 2. In some embodiments, theselection may be further based on features from Table 5. Additionally oralternatively, after making the initial decision, the selector 2006 maydetermine to change the one of transcription units 2014 providing thetranscriptions.

In some embodiments, the selector 2006 may use any of a number ofestimation and classification methods such as machine learning methodsto determine control decisions. Examples of estimation andclassification methods include those listed below in Table 9, amongothers.

TABLE 9 1. LDA (linear discriminant analysis) 2. Linear regression 3.Maximum entropy estimation 4. Maximum entropy modeling 5. Logisticregression 6. Neural networks (including variations such as DNNs, CNNs,LSTMs, etc.) 7. Finite state transducers 8. Kernel methods such assupport vector machines (“SVMs”) 9. Gaussian mixture models (“GMMs”) 10.Table lookups 11. Set of rules 12. Decision trees 13. Random forests 14.Weighted sum of features 15. Transformed features (see FIGs. 27a and27b) 16. Deep belief networks, Boltzmann machines, and other deeplearning methods

In some embodiments, the selector 2006 may use estimation andclassification methods for which training may be performed. An exampleoperation of the environment 2000 describing training the selector 2006using machine learning is now provided. The operation is defined withrespect to processes 1-7 provided below. Modifications, additions, oromissions may be made to the processes 1-7 without departing from thescope of the present disclosure. For example, the processes may beimplemented in differing order. Additionally or alternatively, two ormore processes may be performed at the same time. Furthermore, theoutlined processes and actions are only provided as examples, and someof the processes and actions may be optional, combined into fewerprocesses and actions, or expanded into additional processes and actionswithout detracting from the essence of the disclosed example. Processes1-7 may include:

-   -   1. Define an output for the selector 2006. The output may be,        for example, a transcription accuracy estimate, a decision of        whether to use a revoicing for transcription or to not use        revoicing, a transcription unit configuration (see Table 1) or        selection, a voting decision in a fuser, a determination to        alert a CA of a possible error or to correct the error, a        measure of or a refinement to an agreement or disagreement rate,        a weight or severity assigned to a transcription error, or a        determination that a piece of data contains sensitive        information.    -   2. Select a set of training data samples. Data samples may be,        for example, audio samples, data extracted from log files such        as log files from a transcription service, transcriptions from        revoiced and non-revoiced transcription units, etc.    -   3. Determine one or more target values associated with each        training data sample. A target value may be the desired output        from the selector 2006 for each training data sample. Target        values may be labeled automatically, under human supervision, or        a combination thereof. For example, in estimating accuracy, the        target or desired accuracy output by selector 2006 corresponding        to each data sample may be determined using labels assigned by        humans    -   4. Select a set of one or more features, such as features from        Table 2 and Table 5, to be extracted from data samples and        applied to the input of the selector 2006.    -   5. Associate a set of feature values for the set of features        with each training data sample. For example, if audio samples        are used as data samples, a feature may be a confidence estimate        from an ASR system. Values for the feature may be determined by        processing each training audio sample with the ASR system and        reading a confidence estimate from the ASR output. For each set        of feature values, the selector, classifier, or estimator may        generate an output.    -   6. Select a cost function such as mean squared error, mean        absolute error, or cross entropy. The cost function may be        derived from the output and the target. For example, if a target        is ASR accuracy and the output is estimated ASR accuracy, the        cost may be the squared difference between estimated ASR        accuracy and true ASR accuracy.    -   7. Use a machine learning method, such as one in Table 9, to        train a selector, classifier, or estimator to use the set of        features to determine an output that is close to the target, as        measured by the cost function.

Modifications, additions, or omissions may be made to the environment2000 without departing from the scope of the present disclosure.

FIG. 21 illustrates another example environment 2100 for selectingbetween transcriptions, in accordance with some embodiments of thepresent disclosure. The environment 2100 includes scorers 2116 includinga first scorer 2116 a, a second scorer 2116 b, a third scorer 2116 c, afourth scorer 2116 d, a fifth scorer 2116 e, and a sixth scorer 2116 f.The environment 2100 also includes ASR systems 2120, including a firstASR system 2120 a, a second ASR system 2120 b, a third ASR system 2120c, a fourth ASR system 2120 d, and a fifth ASR system 2120 e. Theenvironment 2100 also includes a transcription unit 2114, a CA client2122, and a selector 2106.

In some embodiments, audio, for example from a communication session,may be provided to the CA client 2122, the transcription unit 2114, thefirst ASR system 2120 a, and the second ASR system 2120 b. Thetranscription unit 2114, the first ASR system 2120 a, and the second ASRsystem 2120 b may be configured to generate transcriptions using theaudio and provide the transcriptions to various scorers 2116 asillustrated.

In some embodiments, the CA client 2122 may generate revoiced audio andprovide the revoiced audio to the third ASR system 2120 c, the fourthASR system 2120 d, and the fifth ASR system 2120 e. The third ASR system2120 c, the fourth ASR system 2120 d, and the fifth ASR system 2120 emay be configured to generate transcriptions using the revoiced audioand provide the transcriptions to various scorers 2116 as illustrated.

In some embodiments, the transcription unit 2114 may be a revoicedtranscription unit. In some embodiments, the fifth ASR system 2120 e maybe speaker-dependent based on the speaker revoicing the audio andinterfacing with the CA client 2122. The other of the ASR systems 2120may be speaker-independent. In these and other embodiments, each of theother ASR systems 2120 may include the same or different configurationsof ASR models.

In some embodiments, each of the scorers 2116 may determine agreementrates between the respective transcriptions obtained and may provide theagreements to the selector 2106. The agreement rates between varioustranscriptions as determined by the scorers 2116 may be used as inputfeatures to the selector 2106. The selector 2106 may be analogous to theselector 2006 of FIG. 20 and may use the input features to determinecontrol decisions.

Although depicted as the selector 2106 obtaining the agreement ratesfrom all of the scorers 2116, in some embodiments, one or more of theASR systems 2120 may not be used to generate transcriptions that may beselected for presentation to a party participating in a communicationsession generating the audio illustrated in the environment 2100. Inthese and other embodiments, the transcriptions and other output of theASR systems 2120 may be used as input features for the selector 2106 andused by the selector 2106 to determine control decisions. In these andother embodiments, when a transcription or output of an ASR systemoutput is used for selection and not for presentation, the ASR systemmay be run in a reduced mode (i.e., “crippled mode”) that consumes fewercompute resources and may deliver relatively lower accuracy.

In some embodiments, one or more of the ASR systems 2120 may generateadditional information such as:

-   -   1. Alternate transcriptions in the form of an n-best list, WCN,        lattice, etc.;    -   2. Confidence scores or accuracy metrics; and    -   3. Meta-information on acoustic or ASR parameters such as beam        width, CPU usage, signal characteristics, or perplexity scores.

The additional information may be provided to the selector 2106 for usein determining control decisions. Additionally or alternatively, theselector 2106 may use other features, such as one or more itemsdescribed in Table 2 and Table 5, as input in determining controldecisions.

The environment 2100 illustrates various configurations of ASR systemsand how the transcriptions of the ASR systems may be compared todetermine agreement rates. The agreement rates of various ASR systemsmay also be used for other purposes besides being provided to theselector 2106. For example, the comparison between transcriptions may beused for accuracy estimation purposes of ASR systems, for determiningdifficulty of transcribing the audio, for determining whichtranscription to select when fusing outputs from multiple transcriptionunits, or for classification, among other purposes. Classification mayrefer to determining that a transcription or a system that may generatethe transcription may be used for a particular purpose, such as any ofthe uses for systems and/or transcriptions described in this disclosure.For example, classification may include classifying transcription unitsinto different classes such that a transcription unit from anappropriate class may be selected for a particular situation.

For example, the fourth ASR system 2120 d may be a speaker-independentASR system trained on a population of callers. The third ASR system 2120c may be a speaker-independent ASR system trained on multiple CA voicesamples. The second scorer 2116 b then may provide a feature thatreflects the agreement between the fourth ASR system 2120 d and thethird ASR system 2120 c. As another example, in some embodiments, thesecond ASR system 2120 b may be “crippled,” or configured for loweraccuracy than the first ASR system 2120 a (see FIG. 13). The first ASRsystem 2120 a vs. the second ASR system 2120 b agreement rate may beused as a measure of the difficulty of transcribing particular audio. Itmay also be used to predict the accuracy of the non-revoiced ASRsystems, the revoiced ASR systems, and other transcription units.

As another example, the outputs of the second scorer 2116 b and thethird scorer 2116 c may be used to estimate accuracy of the revoicingprovided by the CA client 2122. Alternatively or additionally, theoutputs of multiple scorers 2116 such as fourth, fifth, and sixthscorers may be used to estimate revoiced or non-revoiced ASR systemaccuracy such as the accuracy of the first ASR system 2120 a.Alternatively or additionally, the output of the fourth scorer 2116 dmay be used to estimate non-revoiced ASR system accuracy. (see FIG. 19).

As another example, the outputs of the fourth scorer 2116 d and thefifth scorer 2116 e may be used to estimate ASR accuracy of thenon-revoiced ASR systems. Alternatively or additionally, the second ASRsystem 2120 b may use the transcription of the first ASR system 2120 aor the fifth ASR system 2120 e as a grammar. The audio input to thesecond ASR system 2120 b may be delayed so that the grammar is in placebefore corresponding audio is received by the second ASR system 2120 b.Running the second ASR system 2120 b with such a grammar may increasethe likelihood that the second ASR system 2120 b generates the sametranscription as the first ASR system 2120 a or the fifth ASR system2120 e, respectively. The fifth scorer 2116 e and the sixth scorer 2116f may then be used to estimate revoiced or non-revoiced ASR systemaccuracy. Alternatively or additionally, the output of first scorer 2116a and other agreement rates between one or more revoiced ASR systems maybe used to measure the revoicing accuracy and/or the accuracy of thefifth ASR system 2120 e and to estimate the difficulty of transcribingparticular audio or audio from a particular participant in acommunication session.

In some embodiments, the depicted environment 2100 may use ASR systemsthat generate results (i.e., transcriptions) with error patterns thatare uncorrelated, that differ in accuracy, or that provide differencesused in improving or predicting accuracy. Examples of how two ASRsystems may be configured or trained differently for this purpose arelisted in Table 3. By providing transcriptions to the selector 2106 thatdiffer and thus a greater diversity of information, the selector 2106may be configured to improve the process of determining controldecisions. As described above, resources may be shared across ASRsystems (see FIG. 6).

Modifications, additions, or omissions may be made to the environment2100 without departing from the scope of the present disclosure. Forexample, in some embodiments, transcriptions generated by one or more ofthe ASR systems 2120 may be combined, e.g., fused, to generate thetranscriptions that are provided to the scorers 2116. For example, thetranscriptions of the first ASR system 2120 a and the second ASR system2120 b may be fused. Alternatively or additionally, the transcriptionsof the third ASR system 2120 c, the fourth ASR system 2120 d, and thefifth ASR system 2120 e may be fused. Alternatively or additionally, thetranscriptions of the third ASR system 2120 c and the fourth ASR system2120 d may be fused. Alternatively or additionally, the transcriptionsof one or more revoiced and speaker-independent ASR systems may be fusedwith transcriptions from one or more non-revoiced speaker-dependent ASRsystems.

As another example, the environment 2100 may not include one or more ofthe scorers 2116 and/or one or more of the ASR systems 2120. As anotherexample, the transcription of each of the ASR systems 2120 and thetranscription unit 2114 may be compared together by a scorer to generatea complete set of agreement rates that may be provided to the selector2106.

FIG. 22 is a schematic block diagram depicting an example embodiment ofa scorer 2216, in accordance with some embodiments of the presentdisclosure. In some embodiments, the scorer 2216 may be an exampleimplementation of the scorers 2116 of FIG. 21 or the scorer 2016 of FIG.20. The scorer 2216 may be configured to evaluate similarity between twotoken strings, such as two transcriptions. In some embodiments, thescorer 2216 may compare hypotheses transcriptions, from transcriptionunits or ASR systems, as illustrated in FIGS. 20 and 21. In these andother embodiments, the output of the scorer 2216 may be referred to asan agreement rate. In some embodiments, the scorer 2216 may compare areference transcription (i.e., a transcription assumed to be correct)and a hypothesis transcription. In these and other embodiments, theoutput of the scorer 2216 may be referred to as an accuracy score withrespect to the accuracy of the hypothesis transcription with respect tothe reference transcription.

In some embodiments, the scorer 2216 may include first and seconddenormalizers 2202 a and 2202 b. The first and second denormalizers 2202a and 2202 b may be configured to convert one or both token strings to acommon format, as disclosed in the description of FIG. 14. The commonformat may include an unambiguous format that can only beread/interpreted one way. For example, denormalizing an address renderedas “123 Lake Shore Dr.,” where “Dr.” may refer to “drive” or “doctor,”may yield “one twenty three lake shore drive.” In some embodiments, oneor both of the first and second denormalizers 2202 a and 2202 b may notbe included. For example, when the token strings have not beennormalized, the first and second denormalizers 2202 a and 2202 b may notbe included as no denormalization may be performed. In another example,the first denormalizer 2202 a may be configured to convert a referencetranscription to a structure that represents multiple formats and ahypothesis transcription may be presented to an aligner 2204 withoutdenormalization. In this and other embodiments, the first denormalizer2202 a may convert a text segment to a structure listing multipleformats and the aligner 2204 and error counter 2206 may be configured toconsider a hypothesis transcription as matching any of the multipleformats. For example, the first denormalizer 2202 a may incorporate arule such as “{Cathy, Kathy, Kathie}”=>“{Cathy, Kathy, Kathie},”indicating that the words “Cathy,” “Kathy,” or “Kathie” are eachconverted to the structure “{Cathy, Kathy, Kathie}.” The aligner 2204and error counter 2206 may then consider any of the words “Cathy,”“Kathy,” or “Kathie” in the hypothesis transcription as equivalent tothe “{Cathy, Kathy, Kathie}” structure appearing in the referencetranscription.

In some embodiments, the scorer 2216 may include an aligner 2204configured to align two or more transcriptions in a manner that reducesthe number of differences between similar tokens in the transcriptions.The aligner 2204 may obtain the output of the first and seconddenormalizers 2202 a and 2202 b and align the outputs. The aligner 2204may align the outputs of the first and second denormalizers 2202 a and2202 b in a manner analogous to the alignment performed when fusingtoken strings as described in this disclosure.

In some embodiments, the aligned token strings may be provided to anerror counter 2206. The error counter 2206 may count the number ofdifferences between the aligned token strings and a number of tokensthat are the same. The differences may be referred to as errors. Thetokens that are the same, may be referred to as agreements. Thedifferences may include where one token string includes a token theother does not have and where each token string includes the same numberof tokens, but some of the tokens are different. When some of the tokensare different, this may be referred to as substitution. When one tokenstring includes a token another token string does not have, this may bereferred to as a deletion or insertion based on which token string isconsidered the reference token string. When the reference token stringdoes not include the token and the other token string does, this may bereferred to as insertion. When the reference token string includes thetoken and the other token string does not, this may be referred to asdeletion. In these and other embodiments where error types such asinsertions, deletions, and substitutions are counted, a reversal errortype may be added. A reversal error may be determined from the number ofwords in text strings that are swapped. In some embodiments, the swappedtext strings may be adjacent. For example, “I don't really like peas”transcribed as “I really don't like peas” may contain one reversalerror, since “really” and “like” are swapped. In another example, “I'mlate because late last night my car died” transcribed as “I'm latebecause my car died late last night” may be counted as three reversalerrors because two strings of three words each are swapped. In these andother embodiments, the total error rate may be determined by adding thenumber of insertion, deletion, substitution, and reversal errors.

In some embodiments, the error counter 2206 may count all of the errorsand all agreements. A comparison of the errors to the agreements may bereported as an agreement rate, accuracy, or error rate. Additionally oralternatively, the different types of errors such as deletions,substitutions, and insertions, may be counted and reported separately togenerate a detailed output. Modifications, additions, or omissions maybe made to FIG. 22 and/or the components operating in FIG. 22 withoutdeparting from the scope of the present disclosure. For example, thescorer 2216 may not include the first and second denormalizers 2202 aand 2202 b.

FIG. 23 is a schematic block diagram depicting another exampleembodiment of a scorer 2316, in accordance with some embodiments of thepresent disclosure. In some embodiments, the scorer 2316 may be anexample implementation of the scorers 2116 of FIG. 21 or the scorer 2016of FIG. 20. In some embodiments, the scorer 2316 may compare hypothesestranscriptions, from transcription units or ASR systems, as illustratedin FIGS. 20 and 21.

In some embodiments, the scorer 2316 may include first and seconddenormalizers 2302 a and 2302 b and an aligner 2304, which may beanalogous to elements in the scorer 2216 previously described in FIG.22. The output of the aligner 2304 may be provided to the error detector2306. The error detector 2306 may provide an indication of an errorbetween the token strings. The error detector 2306 may identify theerrors in a similar manner as an error counter 2206 of FIG. 22. Theerror detector 2306 may provide to an integrator 2302 an indication whenan error is identified. The integrator may be configured to count oraverage the number of errors to generate an error rate. The error ratedetermined by the integrator 2302 may be a cumulative count, a count oraverage over a fixed interval of time, or a decaying average. Theintegrator 2302 may communicate the error rate to an adjuster 2303.

In some embodiments, the error rate may represent the errors of thesecond transcription received by the second denormalizer 2302 b withrespect to the first transcription received by the first denormalizers2302 a. In these and other embodiments, however, the first transcriptionmay not be a reference transcription. For example, the secondtranscription may be from a regular ASR system and the firsttranscription may be from a revoiced ASR system. As a result, the firsttranscription may include errors. Thus, the differences between thesecond transcription and the first transcription does not necessarilymean that the second transcription includes a true error as the secondtranscription may be correct and the first transcription may beincorrect, but because of the difference in the transcriptions, theerror detector 2306 may indicate an error in the second transcription.In these and other embodiments, the adjuster 2303 may adjust the errorrate to compensate for the errors in the first transcription. Forexample, in some embodiments, the adjuster 2303 may add a correctionfactor 2308 to the error rate. The correction factor 2308 may be basedon the negative value of the average error rate of the firsttranscription. Alternatively or additionally, the adjuster 2303 may alsoadjust the error rate based on other features 2310. The other features2310 may include one or more items from Table 2 and Table 5. The outputof the adjuster 2303 may be an estimated error rate 2312, which may bethe error rate output by the scorer 2316.

Modifications, additions, or omissions may be made to the scorer 2316without departing from the scope of the present disclosure. For example,the adjuster 2303 may be replaced by an estimator such as the estimatordescribed below with reference to FIGS. 24, 27 a, and 27 b, and may useother estimation methods such as those listed in Table 9.

FIG. 24 is a schematic block diagram illustrating an example embodimentof a selector 2406, in accordance with some embodiments of the presentdisclosure. In some embodiments, the selector 2406 may include anestimator 2402, a comparator 2404, and a threshold 2410. In general, theselector 2406 may be configured to determine control decisions asdiscussed with respect to the selectors 2006 and 2106 of FIGS. 20 and21.

The estimator 2402, in some embodiments, may be configured to receivevalues for one or more input features 2408. Based on the values of theone or more input features 2408, the estimator 2402 may determine anestimate for a parameter upon which the selector 2406 may determine acontrol decision. The parameter may include a confidence score regardinga transcription, an accuracy of a transcription, latency betweentranscriptions, other metrics related to a transcription, and any metricthat may be used to select between a revoiced or non-revoicedtranscription unit/ASR system, among others. Examples of input features2408 include an agreement rate from a scorer, such as a scorer 2016 ofFIG. 20, the features discussed with respect to the selector 2006 ofFIG. 20, and the features described above with reference to Table 2 andTable 5, among others.

In some embodiments, the estimated parameter may be transmitted to thecomparator 2404. The comparator 2404 may be configured to compare theestimate with a threshold 2410. Based on the comparison, the selector2406 may determine a control decision. For example, in response to theestimated parameter satisfying the threshold 2410, the selector 2406 maydetermine to direct a revoiced ASR system to generate transcriptions. Inresponse to the estimated parameter not satisfying the threshold 2410,the selector 2406 may determine to direct a non-revoiced ASR system togenerate transcriptions. In some embodiments, the threshold 2410, incombination with other factors, may contribute to an automation rate ofa transcription system or portion of a transcription system. In theseand other embodiments, the automation rate may include a percentage ofthe total transcriptions that are generated by a non-revoiced ASR systemas compared to a revoiced ASR system.

FIG. 25 is a schematic block diagram illustrating an example embodimentof a selector 2502, in accordance with some embodiments of the presentdisclosure. In some embodiments, the selector 2502 may be configured todetermine control decisions as discussed with respect to the selectors2006 and 2106 of FIGS. 20 and 21. For example, the control decisions maybe to select between different transcription units to generatetranscriptions for audio.

In some embodiments, the transcription units may include any number ofdifferent configurations. For example, the transcription units may beconfigured as revoiced transcription units, non-revoiced transcriptionunits, combination of revoiced and non-revoiced transcription units,transcription units with fusers, among other combinations such asdescribed in Table 1. Alternatively or additionally, the transcriptionunits, as discussed previously, may be software based such that they maybe instantiated and torn down as directed. In these and otherembodiments, the selector 2502 may be configured to select amongtranscription units that are instantiated. Alternatively oradditionally, the selector 2502 may be configured to select amongtranscription unit templates that may be created and directinstantiation of a selected transcription unit.

In some embodiments, the selector 2502 may be configured to obtain inputfeatures 2508. The input features 2508 may be analogous to the inputfeatures 2408 of FIG. 24 and may include features such as ASR accuracy,agreement rates, and other items in Table 2 and Table 5. Using the inputfeatures 2508, the selector 2502 may select a type of transcription unitbased on the selection parameters 2504 in the selector 2502. In theseand other embodiments, the selection parameters 2504 may inform thedecision making process of the selector 2502. For example, for aparticular input feature and first values for the selection parameters2504, the selector 2502 may select a first transcription unit type.However, for the particular input feature and second values for theselection parameters 2504, the selector 2502 may select a secondtranscription unit type. Thus, the selection parameters 2504 and thevalues of the selection parameters 2504 may determine a type oftranscription unit selected based on input features. In someembodiments, the input features 2508 may be viewed as informationderived from the current communication session and its participants(e.g. estimated error rate, historical accuracy, etc.), the output ofthe performance tracker 2510 may be viewed as representing theoperational state (i.e. operations metrics) of the system providingservice (including transcription units, servers, network connections,etc.), and selection parameters may be viewed as rules (derived frombusiness decisions and the operational state) to be used in theselection process. This method of viewing the elements of FIG. 25 is notintended to recite strict definitions, but may be useful inunderstanding the general operation of selector 2502.

In some embodiments, the selection parameters 2504 may include: (1) aperformance threshold (see FIG. 24); (2) a maximum period of time acommunication session may be transcribed using a revoiced ASR system(e.g., the first 10 minutes of a communication session may be eligiblefor transcription by a revoiced ASR system, thereafter, thecommunication session may be transcribed using a non-revoiced ASRsystem); (3) a list of account types (see Table 10 below for a list ofexamples of account types) to be transcribed using a non-revoiced ASRsystem; (4) a list of account types (see Table 10 below) to betranscribed using a revoiced ASR system; and (5) the minimum number ofeach type of transcription units (e.g. revoiced transcription units) tobe held in reserve for handling spikes in request for transcriptions.The account type may be determined, for example, using a phone number orother identifier obtained, for example, using ANI or DNIS or from thenumber dialed by the subscriber or another party.

TABLE 10 1. Business communication sessions 2. Residential communicationsessions 3. Calls to/from voicemail mailboxes (for listening tovoicemail) 4. Calls forwarded to voicemail (for leaving voicemail) 5.Calls forwarded to another number 6. 900 or other premium-ratecommunication session 7. Emergency communication session (e.g., 911communication sessions, poison control) 8. Close family membercommunication session as determined, for example, by matching last nameson the account 9. Frequently called numbers 10. Government numbers 11.Toll-free or 800 numbers 12. Calls to/from a customer care site 13.Calls to/from technical support 14. Calls to/from the caption provider'scustomer care or technical support 15. IVR systems 16. Medical (e.g.,hospital, doctor's office) numbers 17. Cell/mobile phones 18. Landlinephones 19. VoIP communication sessions 20. Video communication sessions21. Communicator watch, glasses, or other wearable devices 22.International numbers 23. Numbers designated as important by thesubscriber 24. Account type is unknown (phone number is available) 25.Phone number is not available 26. Calls answered by music 27. Callsanswered by a recording 28. Calls to/from invalid numbers or numbersthat cannot be dialed 29. Calls to/from numbers that are substantiallynever answered 30. International communication sessions 31. Callsto/from a specific country 32. Conference communication sessions 33.Test communication sessions 34. Calls to/from numbers that ring busy 35.Calls that result in a reorder, SIT, fast busy, all trunks busy, out ofservice tone, or other communication session progress indicators 36.Calls translated from a first language into a second language 37. Callswhere one or more parties hang up, but the communication session is notdisconnected 38. Calls with no audio or with substantially silent audio39. Calls to/from a fax machine, modem, or other non-voice service 40.Calls with a history of being shorter, on average, than a selectedthreshold 41. Calls with a history of being longer, on average, than aselected threshold 42. Calls exhibiting erroneous or anomalous behaviorsuch as an immediate hang-up. 43. Calls identified by the subscriber asbelonging to a defined category such as friends, family, send tovoicemail, do not answer, entities the subscriber does not wish to talkto, medical providers, numbers related to work, numbers related to ahome business, etc. 44. Calls received on an alternate line. Forexample, if the subscriber has a first number such as a home number andsecond number such as a work number, communication sessions receivedfrom callers dialing the first number may be assigned a first accounttype and communication sessions received from callers dialing the secondnumber may be assigned a second account type. 45. Calls where caller IDis blocked or unknown 46. Calls on the subscriber's speed dial list 47.Calls where callers are advised that communication sessions may berecorded or where callers are asked for consent to record. 48. Callsto/from prisons or prison inmates. 49. Calls to/from hospital patientsor rest home residents. 50. Calls to/from numbers associated with socialmedia accounts. 51. Calls to/from software phones such as softphones orsmartphone apps. 52. Calls to/from a specified business or company. Ause case for this feature may include using a language model forcommunication sessions to/from a given company that includes productnames or acronyms related to tire company's business. 53. Calls to/froma service provided by a specified service provider such as a specifiedtelephone carrier or other communications service. A use case for thisfeature may include using a language model trained on data from a givenservice provider. For example, a communication service designed forsales representatives may be transcribed using models adjusted fortopics that include sales terminology. 54. Other

In some embodiments, the values of the selection parameters 2504 may bedetermined based on one or more business objectives. Example businessobjectives are provided in Table 11 below.

TABLE 11 1. Increase overall average accuracy or achieve a minimumtarget. 2. Increase automation rate or achieve a minimum target. 3.Reduce latency or achieve a maximum target. 4. Achieve target values formetrics derived from features in Table 2. Objectives derived from Table2 features include, for example, projected CA capacity (#6), average ormaximum revoiced ASR system idle time (#10), maximum error rate ofrevoiced ASR systems (#15-18), cost of providing service (#28-29), andtime required to add ASR resources (#33). 5. Use all av ailable CAs at agiven time. The number of available CAs may be defined to take intoaccount the number of CAs logged in, staff breaks, idle timerequirements, the number of CAs who could become available within aspecified period of time, a CA pool held aside for contingencies such astraffic spikes, and other operations or personnel-related factors. 6.Ensure that traffic volumes sent to revoiced ASR systems remain withinthe capacity of the available revoiced ASR systems. 7. Deliver accuracyat a selected level, such as a level derived from an estimate ofrevoiced ASR system accuracy. For example, a selection criteria may beadjusted to obtain accuracy, at a minimum cost, that meets or exceedsaccuracy provided by revoiced ASR systems. The selected level may bedetermined using estimated average revoiced ASR system accuracy andestimated average non-revoiced ASR accuracy. 8. Deliver a performancelevel set using one or more performance requirements. For example, if alaw or regulation includes a requirement to deliver a specifiedaccuracy, averaged over a specified period of time and cites a penaltyfor falling below an accuracy minimum, the performance level may beresponsive to the requirement and penalty. 9. Define one or morebusiness objectives based on a combination, such as a weighted sum, ofother business objectives. 10. Generate one or more functions or datapoints and present the information in the form of charts, tables, dials,or other visual indicators. Provide a means, such as via a GUI, for anoperator to view the indicators and select a business objective. Forexample, a GUI may display a chart, such as a table or an ROC curve,showing overall accuracy vs. automation rate and allow the operator toselect an automation rate. The selected automation rate then may becomea business objective. 11. Adjust a threshold and/or set of parametersthat vary over time within a measurement time window to meet a set ofone or more criteria across a time window. Example criteria may includecost, staffing requirements, latency, speed of answer, hardwareutilization, language coverage, word accuracy, punctuation andcapitalization accuracy, and consistency of performance across a varietyof users. Example implementations include the following: a. Thethreshold or parameters may be set to reduce the cost of providingtranscriptions while maintaining a minimum allowable accuracy, whereaccuracy is averaged over a selected measurement window. b. Thethreshold or parameters may vary in response to communication sessiontraffic, revoiced ASR system availability, and other factors, in orderto achieve or adjust selected statistics over time. For example, duringa first period of time when the revoiced ASR system availability isrelatively high and communication session traffic is relatively low, athreshold may be automatically adjusted in one direction to send moretraffic to revoiced ASR systems, potentially increasing accuracy andcost over a first period of time. During a second period of time, whenthe revoiced ASR system availability is relatively low and communicationsession traffic is relatively high, a threshold may be automaticallyadjusted in the opposite direction to send more traffic to revoiced ASRsystems, potentially decreasing accuracy and cost over a second periodof time. One or more selected statistics may be determined across a timespan that includes both periods of time. In one scenario, thresholdsettings may be adjusted over time to reduce the average cost and ensurethat the average accuracy meets a selected minimum, where cost andaccuracy are averaged over both time periods. In an alternate scenario,threshold settings may be adjusted to increase the average accuracyunder a constraint of remaining below a selected maximum cost, wherecost and accuracy are averaged over both time periods. c. A blendedmetric may be defined that includes components related to one or morecost metrics and one or more performance metrics. For example, theblended metric may include a weighted sum of the error rate, latency,total revoiced ASR system cost, and total non-revoiced ASR system cost.The threshold and parameters may be set to values, which may vary overtime, that are projected to reduce or increase the blended metric. 12.Allow speech recognition to take over if the CA stops providingrevoicing or if the error rate of a revoiced ASR system rises above aselected threshold.

In some embodiments, the values of the selection parameters 2504 may befurther determined in response to operations data. Operations data, insome embodiments, may include communication session records, statistics,and measurements or projections for: revoiced ASR system availability,availability and distribution of non-revoiced ASR system or revoiced ASRsystem skills such as spoken languages, missed communication sessions,abandoned communication sessions, test communication sessions, speed ofanswer for incoming communication sessions, automation rate,transcription latency, the number of communication sessions with noaudio, communication sessions with no audio sent to revoiced ASRsystems, numbers and status for sales leads, server load (e.g., CPUload, memory usage), billing status, the number and type of provisionedsystems such as non-revoiced ASR systems and revoiced ASR system,traffic load, networks or equipment out of service, action taken byoperation administrators, alarms, and operation metrics listed inTablet.

In some embodiments, the selector 2502 may be configured to selectparameters and values for parameters. An example of the selector 2502selecting parameters and values for parameters is now provided. Theselection may be defined with respect to processes 1-8 provided below.Modifications, additions, or omissions may be made to the processes 1-8without departing from the scope of the present disclosure. For example,the processes may be implemented in differing order. Additionally oralternatively, two or more processes may be performed at the same time.Furthermore, the outlined processes and actions are only provided asexamples, and some of the processes and actions may be optional,combined into fewer processes and actions, or expanded into additionalprocesses and actions without detracting from the essence of thedisclosed example:

-   -   1. Define one or more global metrics that are responsive to one        or more criteria. The criteria may be one or more of the        business objectives listed above. An example of a global metric        may be a cost function (function1), which may be, for example, a        weighted sum of (a) an average percentage error rate for the        service and (b) a cost in monetary units such as dollars to        provide the service over a selected period of time. (The term        “global” denotes that the metric may encompass multiple        objectives.)    -   2. Identify or define one or more adjustable parameters that may        affect service performance against the global metric. Global        metrics and adjustable parameters may include constraints such        as the maximum number of available revoiced ASR system or the        minimum allowable transcription accuracy. A parameter may also        be defined to be a function of other parameters. For example, a        composite parameter (parameter1) may be defined as the weighted        sum of an ASR beam width (which may trade off the cost and        accuracy of an ASR system) and an accuracy threshold below which        communication sessions are sent to the revoiced ASR system and        above which communication sessions are sent to a non-revoiced        ASR system (which may trade off the cost for the revoiced ASR        system against overall accuracy in a system that transcribes        communication sessions using revoiced ASR systems and        non-revoiced ASR systems).    -   3. The selector 2502 uses a prediction function with an input        including features such as features from Table 2 and Table 5 to        predict the value of the global metric over a range of parameter        settings. For example, the prediction function may be a curve        showing the performance of the global metric function1 as        parameter1 varies. In another example, the prediction function        may plot the average transcription accuracy vs. the average        automation rate for a transcription service.    -   4. The selector 2502 may determine a set of parameter values        that increases or reduces (whichever is favorable) the global        metric. For example, the STT selector may determine a value for        a parameter including an accuracy threshold for selecting a        non-revoiced ASR system or a revoiced ASR system to caption        communication sessions that reduces a global metric, where the        global metric includes the projected cost of providing service        under the constraint that the average transcription accuracy not        fall below a selected percentage.    -   5. The selector 2502 may set operating parameters to the values        determined in step #4.    -   6. A performance tracker 2510 may determine a measured value of        the global metric, such as by tracking performance of the        captioning service over a select period of time, using the        operating parameters from #4, and may compare the measured value        to the predicted value.    -   7. Using the comparison between the predicted and measured        value, the selector 2502 may adjust the method, such as by        adjusting parameters or values of parameters defined within the        method or by using a different set of features. The adjustment        may be performed with the objective to bring the compared values        closer.    -   8. In some embodiments, on a selected schedule or based on        selected events, repeat steps 4-5. Additionally or        alternatively, repeat one or more of steps 1-8.

An example of the above steps (by number) is as follows. (1) Atranscription service provider establishes a global metric of minimizingcost while providing overall accuracy at or above a specified level and(2) defines an ASR accuracy threshold, below which communicationsessions are sent to a revoiced ASR system. (3) The selector 2502estimates the relationship between the threshold and the global metricand (4) determines a value for the threshold predicted to satisfy theglobal metric. (5) The selector 2502 uses the threshold value to decidewhether to transcribe each communication session utilizing revoiced ASRsystems or non-revoiced ASR systems. (6) The performance tracker 2510tracks and reports cost and accuracy. (7) The selector 2502 uses thereported cost and accuracy to adjust the threshold value. (8) Theselector 2502 and performance tracker 2510 repeat steps 3-8.

In some embodiments, the steps above may be implemented by automatedsystems (e.g., by the selector 2502 and performance tracker 2510).Additionally or alternatively, the steps above may be implemented by acombination of automated systems and human operators. For example, a setof tools may be configured to enable human operators to control, guide,override, or execute the above steps. Examples of methods implemented bytools may include:

-   -   1. Log operations data, including communication sessions or        seconds transcribed by the non-revoiced ASR system,        communication sessions or seconds transcribed by the revoiced        ASR system, revoiced ASR system and non-revoiced ASR system        availability, non-revoiced ASR system accuracy, revoiced ASR        system accuracy, overall system accuracy, and other metrics        listed in Table 2. Other operations data may include records for        each communication session, including logging information for        the communication session.    -   2. Display and analyze operations data, including determining        statistics, displaying summary information in tables and charts,        and making recommendations.    -   3. Receive business objectives and global metrics automatically        or from an operator.    -   4. Provision resources, including revoiced ASR system and        non-revoiced ASR system resources, automatically or guided by an        operator.    -   5. Receive updated (e.g., added, deleted, or modified) global        metrics and other adjustable parameters automatically or from an        operator    -   6. Receive an updated prediction function automatically or from        an operator.

FIG. 26 is a schematic block diagram illustrating another exampleembodiment of a selector 2606, in accordance with some embodiments ofthe present disclosure. In some embodiments, the selector 2606 mayinclude a first estimator 2602 a, a second estimator 2602 b, and aclassifier 2604. In general, the selector 2606 may be configured todetermine control decisions as discussed with respect to the selectors2006 and 2106 of FIGS. 20 and 21.

In some embodiments, the first estimator 2602 a, the second estimator2602 b, and the classifier 2604 may be machine learning models that havebeen trained to make decisions based on input features 2608. In theseand other embodiments, the first estimator 2602 a, the second estimator2602 b, referred to collectively as the estimators 2602, and theclassifier 2604 may be an example of the implementation of the selector2502 discussed in FIG. 25. For example, the first estimator 2602 a, thesecond estimator 2602 b, and the classifier 2604 may be trained based onsets of input features, such as the input features 2508 discussed inFIG. 25 and according to training rules defined by selection parameters2504.

In some embodiments, the first estimator 2602 a may be trained withrespect to a first type of transcription unit. Thus, the first estimator2602 a may be configured to estimate a value of a particular feature ofthe first type of transcription unit in response to receiving the firstinput features 2608 a. For example, the first estimator 2602 a mayestimate the measured or predicted error rate of a non-revoiced ASRsystem based on the first input features 2608 a.

In some embodiments, the second estimator 2602 b may be trained withrespect to a second type of transcription unit. Thus, the secondestimator 2602 b may be configured to estimate a value of a particularfeature of the second type of transcription unit in response toreceiving the second input features 2608 b. For example, the secondestimator 2602 b may estimate the measured or predicted error rate of arevoiced ASR system based on the second input features 2608 b. In someembodiments, the particular feature estimated by the first estimator2602 a may be different than the particular feature estimated by thesecond estimator 2602 b or the particular features may be the same.Classifier input features 2612 may include features such as items listedin Table 2 or Table 5.

In some embodiments, the classifier 2604 may be trained based on theoutputs of the estimators 2602 and classifier input features 2612. Theclassifier 2604 may be configured to output a control decision based onthe received input. Alternatively or additionally, the classifier 2604may be configured to output a particular value. The particular value maybe compared to a threshold. In response to the particular valuesatisfying the threshold, a control decision may be implemented.

In some embodiments, each of the first input features 2608 a, the secondinput features 2608 b, and the classifier input features 2612 mayinclude one or more agreement rates from a scorer, such as a scorer 2016of FIG. 20, the features discussed with respect to the selector 2006 ofFIG. 20, and the features described above with reference to Table 2 andTable 5, among others. In some embodiments, the first input features2608 a, the second input features 2608 b, and the classifier inputfeatures 2612 may each include different features, the same features, orfeatures may be shared between the first input features 2608 a, thesecond input features 2608 b, and the classifier input features 2612.

An example of the operation of the selector 2606 is now provided. Thefirst estimator 2602 a may estimate the measured or predicted error rateof a non-revoiced ASR system based on the first input features 2608 a.The second estimator 2602 b may estimate the measured or predicted errorrate of a revoiced ASR system based on the second input features 2608 b.The classifier 2604 may use the estimated error rates and the classifierinput features 2612 to generate a revoicing cost. The revoicing cost mayreflect the relative cost of using a revoiced ASR system versus anon-revoiced ASR system and may be expressed in monetary units such asdollars, as a unitless number such as a ratio, in terms of acontribution to a global metric, or using other units. The revoicingcost may be presented to a comparator which compares the revoicing costto a threshold. When the revoicing cost is less than the threshold, thenthe revoiced ASR system may be used to generate transcriptions. When therevoicing cost is more than the threshold, then the non-revoiced ASRsystem may be used.

Alternatively or additionally, the classifier 2604 may be configured tosubtract one error rate from another. If, for example, the threshold iszero, the estimated non revoiced ASR system error rate is 15%, andestimated revoiced ASR system error rate is 3%, then the classifier 2604may output a positive value, such as 12%, that exceeds the threshold andthus indicates that a revoiced ASR system is selected to providetranscriptions. Additionally or alternatively, the first estimator 2602a may estimate non-revoiced ASR system accuracy and the second estimator2602 b may estimate the difference in cost of selecting a non-revoicedASR system instead of a revoiced ASR system. In these and otherembodiments, the classifier 2604 may select between the non-revoiced ASRsystem or revoiced ASR system or output a value that is compared to athreshold to make a selection.

Additionally or alternatively, the estimators 2602 may provide otherinformation to the classifier 2604. In these and other embodiments, theclassifier 2604 may be configured to select among different options,such as types of transcription units for generating transcriptions.Additionally or alternatively, the estimators 2602 may be omitted andthe input features 2608 and classifier input features 2612 may bepresented to the classifier 2604 which generates an output upon which acontrol decision may be based.

FIGS. 27a and 27b illustrate embodiments of a linear estimator 2702 anda non-linear estimator 2704 respectively, in accordance with someembodiments of the present disclosure. The linear estimator 2702 and anon-linear estimator 2704 may be examples of the estimators 2602 of FIG.26.

In some embodiments, the linear estimator 2702 may include weightsassociated with inputs and an adder 2703. The linear estimator 2702 maybe configured to receive a set of inputs, multiply each of the inputs bya weight α1, α2, α3, . . . aN (depicted as “a1,” etc.), sum the weightedinputs using the adder 2703, and output the weighted sum of the inputs.Weights may be determined using optimization methods such as LinearDiscriminant Analysis (LDA), linear regression, logistic regression,stochastic gradient descent, or gradient boosting. As with otherestimators described herein, input features may include one or moreagreement rates from scorers, such as a scorer 2016 of FIG. 20, thefeatures discussed with respect to the selector 2006 of FIG. 20, and thefeatures described above with reference to Table 2 and Table 5, amongothers.

In some embodiments, the non-linear estimator 2704 may be configured totransform the estimation input features, by for example, using anonlinear function. For example, if x and y are inputs and n is a realnumber, then examples of transformations include functions such asx^(n), log(x), x*y, x^(n)+y^(n), x^(y), neural networks, and activationfunctions typically used with neural networks such as sigmoid functions,logistic functions, tanh(x), ReLU, step functions, etc. Alternatively oradditionally, the non-linear estimator 2704 may operate on one input ata time such as with x² or on multiple inputs simultaneously such as withx²+y², and with neural networks. The transformed features, by thefeature transformer 2706, may be applied to the adder 2705 in additionto or instead of the original estimation input features. The inputfeatures may be weighed before being summed using the adder 2705. Inthese and other embodiments, the weights α1, α2, α3, . . . , aN may thenbe determined using methods similar to those of a linear estimator.

A neural network may be used in various embodiments described herein asan estimator, selector, and classifier. In some embodiments, the neuralnetwork may include a set of one or more inputs, nodes, connections, andoutputs. Each node may receive an input from the set of inputs or fromanother node. Connections between nodes may be multiplied by a weight,so that the input to a first node equals the output of a previous nodemultiplied by the weight associated with the connection between the twonodes. Nodes may accumulate the inputs in a summation where thesummation is the total of the outputs of all previous nodes, eachmultiplied by the respective weight of the connection. Nodes may belinear or nonlinear. For linear nodes, the node output may equal the sumof the inputs for that node. For nonlinear nodes, the inputs may betotaled in a summation step, then processed with a nonlinearity oractivation function. Examples of activation functions include linear,tanh, sigmoid, step, ReLU, leaky ReLU, and Gaussian functions.

Additionally or alternatively, nodes in the neural network may beorganized in layers. The neural network may have as few as one layer orit may have multiple layers as in deep neural networks (DNNs). Theneural network may be feed-forward so that all connections send signalstowards the output. The neural network may include feedback or recurrentconnections that send signals to previous layers or backwards towardsthe input as in recurrent neural networks (RNNs). Other topologies arepossible, including gated recurrent units (GRUs), convolutional neuralnetworks (CNNs), temporal convolutional networks (TCNs), pooled layers,long short-term memory (LSTM) networks, bottleneck DNNs, autoencoders,time delay networks (TDNN), ResNet, WaveNet, attention networks such ashierarchical neural attention encoders, neural networks with transferlearning, densely connected neural nets, generative adversarial networks(GANs), or combinations of the above.

FIG. 28 is a flowchart of an example method 2800 of selecting betweentranscription units for a communication session, in accordance with someembodiments of the present disclosure. The method 2800 may be arrangedin accordance with at least one embodiment described in the presentdisclosure. The method 2800 may be performed, in some embodiments, byprocessing logic that may include hardware (circuitry, dedicated logic,etc.), software (such as is run on a general-purpose computer system ora dedicated machine), or a combination of both. In some embodiments, themethod may be performed by the selector 406 of FIG. 4 or other selectorsdiscussed in this disclosure. In these and other embodiments, the method2800 may be performed based on the execution of instructions stored onone or more non-transitory computer-readable media. Although illustratedas discrete blocks, various blocks may be divided into additionalblocks, combined into fewer blocks, or eliminated, depending on thedesired implementation.

The method 2800 may begin with a signal indicating that a communicationsession has initiated or is about to be initiated. At block 2802, it maybe determined whether to use data from the communication session formodeling, such as for training ASR models or to otherwise improveaccuracy for future communication sessions through obtained data, suchas to attain higher accuracy transcriptions. In some embodiments, acommunication session may be selected for modeling randomly, using anactive learning model that selects communication sessions where a moreaccurate transcription is expected to contribute more to improve ASRaccuracy through modeling, or for other reasons. For example, a moreaccurate transcription may be expected to contribute more to improve ASRaccuracy through modeling if the communication sessions are within aspecified range such as near (e.g., +/−10%) the middle of the estimatedaccuracy distribution. When the communication session is not to be usedfor modeling, the method 2800 may proceed to block 2814. When thecommunication session may be used for modeling, the method 2800 mayproceed to block 2804.

At block 2804, it may be determined whether better accuracy isappropriate for the modeling. In some embodiments, the decisionregarding better accuracy for modeling may be based on a number offactors including, 1) random selection of the communication session forbetter accuracy than a single revoicing transcription unit; 2) a moreaccurate transcription of the communication session is expected tocontribute more to improve ASR accuracy through modeling, among otherfactors.

When better accuracy is not selected for the modeling, the method 2800may proceed to block 2812. In block 2812, a revoicing transcription unitmay be selected to generate transcriptions for the communication. Whenbetter accuracy is selected for the modeling, the method 2800 mayproceed to block 2806.

At block 2806, it may be determined if better services are available.Better services may include providing the audio of the communicationsession to additional transcription units. Additional transcriptionunits may be available if the additional transcription units arecurrently available and are projected to be available throughout thecommunication session with a number of extra transcription units forother priorities. In some embodiments, the transcription unitavailability may be estimated from one or more of: current and projectedsize of the transcription unit pool, current and projected traffic, oraverage transcription unit idle time, among others. In these and otherembodiments, the additional transcription units may be revoicing ornon-revoicing transcription units. In some embodiments, the additionaltranscription units may include better ASR systems, such as ASR systemsthat are or may be configured to run in a more expensive, but moreaccurate mode. In these and other embodiments, the outputs of thetranscription units may be fused. Alternatively or additionally, abetter service may include sending a communication session to a CA usinga stenotype to provide transcriptions.

When better services are available, the method 2800 may proceed to block2810. When better services are not available, the method 2800 mayproceed to block 2812. At block 2812, the better services may be used togenerate a transcription of the communication session.

At block 2814, when the communication session is not going to be usedfor modeling, a device identifier for a device of a party beingtranscribed (the “transcription party”) may be obtained. The deviceidentifier may be obtained, through a service such as automatic numberidentification (ANI) service or a digital automatic numberidentification (DNIS) service, or other methods for incomingcommunication sessions. For outgoing communication sessions, the deviceidentifier may be a number or information used by a device to establishthe communication session with the device of the transcription party.

At block 2816, it may be determined if the device identifier or otherinformation indicates that the communication session includes a deviceassociated with a high-priority number. High-priority numbers may bedescribed with respect to item 76 of Table 5. In response to it beingdetermined that the communication session includes a device associatedwith a high-priority number, the method 2800 may proceed to block 2812where a revoicing transcription unit may be used for the communicationsession. In response to it being determined that the communicationsession does not include a device associated with a high-prioritynumber, the method 2800 may proceed to block 2818.

At block 2818, it may be determined if the device of the transcriptionparty has provided audio for which transcriptions have been previouslygenerated. In response to the device providing audio for whichtranscriptions have been previously generated, the method 2800 mayproceed to block 2820. In response to the device not providing audio forwhich transcriptions have been previously generated, the method 2800 mayproceed to block 2822.

At block 2820, prior communication session statistics, models, or otherprofile information related to the device may be retrieved.

At block 2822, a prediction or estimate of non-revoicing ASR systemaccuracy may be determined. The prediction or estimate may be based onavailable information, including estimates from estimators, the deviceprofile (e.g., historical accuracy for the transcription party), otherdevice information, items from Table 2 and Table 5, etc.

At block 2824, it may be determined if the predicted accuracy t_(p) isgreater than a threshold t₁. In response to the predicted accuracy t_(p)being greater than the threshold, the method 2800 may proceed to block2826. In response to the predicted accuracy tp not being greater thanthe threshold, the method 2800 may proceed to block 2828.

At block 2828, it may be determined if a revoicing transcription unit isavailable. If a revoicing transcription is available, the method 2800may proceed to block 2812. Otherwise, the method 2800 may proceed toblock 2826. At block 2826, a non-revoicing transcription unit may beselected to generate transcriptions for the communication session.

Modifications, additions, or omissions may be made to the method 2800without departing from the scope of the present disclosure. For example,the operations of method 2800 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areonly provided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments. As another example, the revoicing ASR systemmay be replaced with a non-revoicing ASR system, such as one withsuperior performance or higher cost.

FIG. 29 is a flowchart of an example method 2900 of selecting betweentranscription units for a communication session, in accordance with someembodiments of the present disclosure. The method 2900 may be arrangedin accordance with at least one embodiment described in the presentdisclosure. The method 2900 may be performed, in some embodiments, byprocessing logic that may include hardware (circuitry, dedicated logic,etc.), software (such as is run on a general-purpose computer system ora dedicated machine), or a combination of both. In some embodiments, themethod is performed by the selector 406 of FIG. 4 or other selectorsdescribed in this disclosure. In these and other embodiments, the method2900 may be performed based on the execution of instructions stored onone or more non-transitory computer-readable media. Although illustratedas discrete blocks, various blocks may be divided into additionalblocks, combined into fewer blocks, or eliminated, depending on thedesired implementation.

At block 2902, a signal may be received indicating that a communicationsession has been or is about to be initiated between a device of asubscriber and a device of a party whose speech is to be transcribed(the “transcription party”).

At block 2904, an account type for the device of the transcription partyin the communication session may be determined. A phone number or otheridentifying information may be considered when determining the accounttype. Other sources of identifying information may include, but are notlimited to, public records or a subscription service containingidentification records. Many different account types may be identified,including but not limited to those enumerated in Table 10.

At block 2904, a type of the account may be analyzed to determine one ofmultiple account groups to which the account type may belong. In theseand other embodiments, in response to determining that the account typematches a first list of account types (e.g., residential or unknowncommunication sessions), the method 2900 may proceed to block 2906. Inresponse to determining that the account type matches a second list ofaccount types, the method 2900 may proceed to block 2908. The secondlist may include, but is not limited to, business communicationsessions, toll-free or 800 numbers, medical (hospital, doctor), IVRsystems, and communication sessions where estimated accuracy was above aselected threshold on a previous communication session. Additionally oralternatively, the second list may include communication sessions tocustomer support, technical support, other customer care centers, orservices where an announcement is played to advise callers thatcommunication sessions may be recorded.

In response to determining that the account type matches a third list ofaccount types that require premium service, the method 2900 may proceedto block 2912. The third list may include, for example emergency numbersand numbers designated by the subscribing party as high-priority (seeitem 76 of Table 5).

In response to determining that the account type matches a fourth listof account types, such as low priority communication sessions or tasksthat may be transcribed at a later time, the method 2900 may proceed toblock 2910.

At block 2906, a revoicing transcription unit may be selected togenerate transcriptions for the communication session. At block 2908, anon-revoicing transcription unit may be selected to generatetranscriptions for the communication session.

At block 2910, an embedded ASR system may be selected to generatetranscriptions for the communication session. Alternatively oradditionally, the audio may be recorded and provided to a queue to betranscribed in non-real time during the communication session. In theseand other embodiments, the embedded ASR system may be implemented on thedevice of the subscribing party, or on a device associated with orconnected to the device participating in the communication session.

At block 2912, a premium transcription unit may be selected to generatetranscriptions for the communication session. A premium transcriptionunit may be a transcription unit that includes multiple ASR systems,multiple CA clients, ASR systems with additional models orconfigurations to generate better transcriptions, network ASR systems,among other types of ASR systems.

In general, after selecting a transcription unit, the method 2900 mayinclude predicting future accuracy of the transcriptions based on thesame or different types of transcription units. Based on thepredictions, the type of transcription unit that may be used to generatetranscriptions may change during the communication session.

At block 2916, accuracy t_(p) of a transcription of the communicationsession generated by a non-revoicing transcription unit may bepredicted. In some embodiments, the accuracy may be predictedcontinuously, periodically, at other intervals, or in response to one ormore factors, such as a length of the communication session, a change inspeakers, a change in audio quality, among other factors. In someembodiments, methods such as those listed in Table 9 and one or more ofthe features in Table 2 and Table 5 may be used to estimate or predictaccuracy. In some embodiments, the predicted accuracy may be the currentcalculated accuracy.

An example is now provided with respect to predicting or estimatingaccuracy of a transcription. In these and other embodiments, one or morecompanion ASR systems may process substantially the same speech as afirst transcription unit. In some embodiments, the first transcriptionunit may be a revoicing ASR system and the companion ASR system may benon-revoiced ASR systems. In some embodiments, the first transcriptionunit system may be a non-revoicing ASR system and the companion ASRsystems may be non-revoiced ASR systems. In these and other embodiments,the accuracy may be estimated using one or more features such as (a) ASRconfidence (from one or more of the companion ASR systems), (b) thedisagreement rate between the companion ASR systems, (c) thedisagreement rate between each companion ASR system and the firsttranscription unit, (d) the number of words from one or more of thecompanion ASR systems where the confidence is above a selectedpercentage. In some embodiments, one or more features from Table 2 andTable 5 may also be used.

In some embodiments, any combination of the first transcription unit andthe companion ASR systems may be substantially identical except for oneaspect. For example, the first transcription unit and one of thecompanion ASR systems may be substantially identical except for oneaspect and the other companion ASR system may be different.Alternatively or additionally, the first transcription unit and thecompanion ASR system may be substantially identical except for oneaspect when there is one companion ASR system. Alternatively oradditionally, the companion ASR systems may be substantially identicalexcept for one aspect. The one aspect may be, for example, selected fromthe n-gram length in the language model, the size or topology of aneural network implementing an acoustic model, the source or size oftraining data in the language model or acoustic model, and distorting orotherwise processing the input speech for one of the ASR systems. Theone aspect may alternatively be a method of crippling one of the ASRsystems.

In some embodiments, any combination of the first transcription unit andthe companion ASR systems may include software that is substantiallyidentical or is derived from a common source, a first ASR system usingat least one model (e.g., an acoustic model and/or language model) thatis different from the corresponding model (i.e., the model is used in asimilar fashion) used by a second ASR system.

In another example regarding predicting or estimating accuracy of atranscription, an accuracy estimator may be trained, using a machinelearning method, such as one in Table 9 and using at least two features,trained on a set of audio samples where the accuracy of each sample islabeled and used as a target for the machine learning method.

At block 2918, the accuracy t_(p) may be compared to a threshold t₁. Insome embodiments, the threshold t₁ may be based on one or more factors.The factors may be similar to the factors used to determine betweenselecting a revoicing transcription unit or a non-revoicingtranscription unit as discussed in this disclosure. For example, athreshold may be determined, for example, by using a measure ofcommunication session transcription difficulty, estimated revoicing ASRsystem accuracy, particular accuracy requirements, and other features.

In response to the accuracy t_(p) satisfying the threshold t₁, themethod 2900 may proceed to block 2932. Otherwise, the method 2900 mayproceed to block 2920, where the original revoicing transcription unitmay continue to generate the transcription. After block 2920, the method2900 may proceed to block 2916 for continued prediction of the accuracyof the transcription of the communication session generated by anon-revoicing transcription unit.

At block 2922, accuracy t_(p) of a transcription of the communicationsession generated by an embedded transcription unit may be predicted. Insome embodiments, the accuracy may be predicted continuously,periodically, at other intervals, or in response to one or more factors,such as a length of the communication session, a change in speakers, achange in audio quality, among other factors. In some embodiments, thepredicted accuracy may be the current calculated accuracy.

At block 2924, the accuracy t_(p) may be compared to a threshold t₃. Inresponse to the accuracy t_(p) satisfying the threshold t₃ the method2900 may proceed to block 2926. Otherwise, the method 2900 may proceedto block 2934.

At block 2934, accuracy t_(p) of the transcription of the communicationsession generated by the non-revoicing transcription unit may becompared to a threshold t₂. In response to the accuracy t_(p) satisfyingthe threshold t₂ the method 2900 may proceed to block 2932 where theoriginal non-revoicing transcription unit may continue to generate thetranscription. After block 2932, the method 2900 may proceed to block2922 for continued prediction of the accuracy of the transcription ofthe communication session generated by an embedded and non-revoicingtranscription unit. Otherwise, the method 2900 may proceed to block 2920where a revoicing transcription unit may begin to generatetranscriptions for the communication session.

At block 2928, accuracy t_(p) of a transcription of the communicationsession generated by an embedded transcription unit may be evaluated. Insome embodiments, the accuracy may be evaluated continuously,periodically, at other intervals, or in response to one or more factors,such as a length of the communication session, a change in speakers, achange in audio quality, among other factors. In some embodiments, thepredicted accuracy may be the current calculated accuracy.

At block 2930, the accuracy t_(p) may be compared to a threshold t₄. Insome embodiments, the threshold t₄ may be based on one or more factors.The factors may be similar to the factors used to determine betweenselecting a revoicing transcription unit or a non-revoicingtranscription unit as discussed in this disclosure.

In response to the accuracy t_(p) not satisfying the threshold t₄, themethod 2900 may proceed to block 2932 where a non-revoicingtranscription unit may begin to generate transcriptions for thecommunication session. Otherwise, the method 2900 may proceed to block2926, where the original embedded transcription unit may continue togenerate the transcription. After block 2926, the method 2900 mayproceed to block 2928 for continued evaluation of the accuracy of thetranscription of the communication session generated by the embeddedtranscription unit.

In some embodiments, the accuracy thresholds (t₁, t₂, etc.) may be thesame or different. To avoid frequent switching between differenttranscription units, t₂ may be set lower than t₁ and t₄ may be set lowerthan t₃. Although the method 2900 is described as calculating accuracyestimates and predictions, in this and other embodiments disclosedherein, current or past estimates may be used in place of predicted orcalculated estimates and vice versa, because past performance may beused to predict future performance.

In some embodiments, the predicted accuracy thresholds (t₁, t₂, etc.)may change depending on how long the communication session has beenmiming and a duration of measurement window for predicting or evaluatingthe accuracy. For example, one or more of the thresholds may have afirst set of values for intervals starting after a first time period(e.g., the first minute of the communication session) and a second setof values for intervals starting at the beginning of the communicationsession. Examples of how accuracy thresholds may be constructed and usedinclude: (1) a threshold may be set to 100% for any 20 seconds after thefirst minute of a communication session or 97% for the first 20 secondsof the communication session; (2) a threshold may be set to 90% for any1 minute after the first minute of a communication session or 88% forthe first 30 seconds of the communication session; or (3) a thresholdmay be set to 80% plus an estimated measurement error. In someembodiments, the estimated measurement error may include an estimate ofthe precision of the accuracy estimation. In example (3), above, if theaccuracy is estimated at 85% and the estimated measurement error is+1-7%, then the threshold may be 80%+7%=87%.

An accuracy threshold may also change based on the account typeassociated with the device identifier. For example, a businesscommunication session may use a first threshold (e.g., t₁=87%) and aresidential communication session may use a second threshold (e.g.,t₁=78%). The account type may be one or more of the items in Table 10.

Modifications, additions, or omissions may be made to the method 2800without departing from the scope of the present disclosure. For example,the operations of method 2800 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areonly provided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

For example, in some embodiments, one or more other groups of accounttypes may be included. In these and other embodiments, thetranscriptions may be generated by one of the above described options oranother type of service. As another example, the revoicing transcriptionunit may be replaced with a non-revoicing transcription unit, such asone with superior performance or higher cost.

FIG. 30 is a flowchart of another example method 3000 of selecting anASR or a CA for transcription of a communication session, in accordancewith embodiments of the present disclosure. The method 3000 may bearranged in accordance with at least one embodiment described in thepresent disclosure. The method 3000 may be performed, in someembodiments, by processing logic that may include hardware (circuitry,dedicated logic, etc.), software (such as is run on a general-purposecomputer system or a dedicated machine), or a combination of both. Insome embodiments, the method is performed by the selector 406 of FIG. 4or other selector described in this disclosure. In these and otherembodiments, the method 3000 may be performed based on the execution ofinstructions stored on one or more non-transitory computer-readablemedia. Although illustrated as discrete blocks, various blocks may bedivided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

The method 3000 may begin at block 3002, where first audio dataoriginating at a first device during a communication session between thefirst device and a second device may be obtained. In these and otherembodiments, the communication session may be configured for verbalcommunication.

At block 3004, an availability of revoiced transcription units in atranscription system may be obtained. In some embodiments, theavailability of revoiced transcription units may be based on one or moreof: a current peak number of transcriptions being generated, a currentaverage number of transcriptions being generated, a projected peaknumber of transcriptions to be generated, a projected average number oftranscriptions to be generated, a projected number of revoicedtranscription units, and a number of available revoiced transcriptionunits. Alternatively or additionally, the availability of revoicedtranscription units may be based on three or more of: a current peaknumber of transcriptions being generated, a current average number oftranscriptions being generated, a projected peak number oftranscriptions to be generated, a projected average number oftranscriptions to be generated, a projected number of revoicedtranscription units, and a number of available revoiced transcriptionunits.

At block 3006, in response to revoiced transcription units beingavailable, the method 3000 may proceed to block 3008. In response torevoiced transcription units not being available, the method 3000 mayproceed to block 3014.

At block 3008, in response to establishment of the communicationsession, a revoiced transcription unit may be selected, based on theavailability of revoiced transcription units, instead of a non-revoicedtranscription unit to generate a transcription of the first audio datato direct to the second device.

At block 3010, revoiced audio generated by a revoicing of the firstaudio data by a captioning assistant may be obtained by a revoicedtranscription unit.

At block 3012, a transcription of the revoiced audio may be generatedusing an ASR engine of the revoiced transcription unit. The ASR enginemay be part of an ASR system. In some embodiments, the automatic speechrecognition engine may be trained specifically for speech of thecaptioning assistant. Block 3012 may be followed by block 3018.

At block 3014, a non-revoiced transcription unit may be selected. Atblock 3016, a transcription of the audio may be generated by thenon-revoiced transcription unit. Block 3016 may be followed by block3018.

At block 3018, the transcription of the revoiced audio may be directedto the second device as the transcription of the first audio data. Insome embodiments, the directing may occur in response to selecting therevoiced transcription unit.

Modifications, additions, or omissions may be made to the method 3000without departing from the scope of the present disclosure. For example,the operations of method 3000 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areonly provided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

For example, in some embodiments, the method 3000 may include afterdirecting the transcription of the revoiced audio to the second deviceand during the communication session, obtaining second audio dataoriginating at the first device during the communication session andobtaining, from the non-revoiced transcription unit using a secondautomatic speech recognition engine, a second transcription of thesecond audio data. In some embodiments, the method 3000 may furtherinclude generating, by the revoiced transcription unit, a thirdtranscription of a revoicing of the second audio data using theautomatic speech recognition engine, generating a fourth transcriptionusing one or more words of the second transcription and one or morewords of the third transcription, and directing the fourth transcriptionto the second device as a transcription of the second audio data.

As another example, the method 3000 may further include during a periodwhen the revoiced transcription unit is generating the transcription ofthe revoiced audio, obtaining, from the non-revoiced transcription unitusing a second automatic speech recognition engine, a secondtranscription of the first audio data.

In these and other embodiments, the automatic speech recognition enginemay be trained specifically for speech of the captioning assistant andthe second automatic speech recognition engine may be trained for aplurality of speakers.

Alternatively or additionally, while directing the transcription of therevoiced audio to the second device and during the communicationsession, the method 3000 may further include determining a differencebetween a first number of words in the revoiced audio and a secondnumber of words in the first audio data is below a comparison thresholdand in response to the difference being below the comparison threshold,directing the second transcription to the second device as thetranscription of the first audio data instead of the transcription ofthe revoiced audio.

Alternatively or additionally, while directing the transcription of therevoiced audio to the second device and during the communicationsession, the method 3000 may further include determining an error rateof the transcription of the revoiced audio is below an error thresholdand in response to the error rate being below the error threshold,directing the second transcription to the second device as thetranscription of the first audio data instead of the transcription ofthe revoiced audio.

FIGS. 31-43, among others, describe various systems and methods that maybe used to generate transcriptions with accuracy that may be higher thantranscriptions generated by a revoiced transcription unit withoutadditional ASR systems or corrections by another CA or a non-revoicedtranscription unit with a single ASR system. In some embodiments, higheraccuracy transcriptions may be generated in real-time where recording ofaudio is not allowed. Higher accuracy transcriptions generated in theseand other embodiments may be used in various situations, including thosedescribed in Table 12 below.

TABLE 12 1. Training ASR systems or ASR models. This may includetraining language models, which may be trained on text transcriptions,and training acoustic models, which may be trained on audio samples andtext transcriptions. High-accuracy transcriptions may also be used forcounting and creating n-grams, which may be used to train languagemodels. 2. Computing average ASR accuracy and using the results forbenchmarking. 3. Computing transcription accuracy of a pool of revoicingtranscription units. 4. Computing accuracy of revoicing transcriptionunits or for one or more individual CAs and using the results fortraining, managing, monitoring, assisting, providing feedback, providingperformance-based incentives. screening applicants, hiring, andterminating. 5. Computing accuracy for an individual revoicingtranscription unit associated with a CA and reporting results to thesupervisor of a CA associated with the revoicing transcription unit. 6.Measuring revoicing transcription units associated with CAs in terms ofaccuracy, latency, and areas of strengths and weaknesses such as topics,accents, languages, and speaker types. These measures may be used inselecting a revoicing transcription unit to transcribe a givencommunication session and in making a selection decision regardingtranscription units. 7. Providing transcriptions to subscribers fordifficult or high-priority communication sessions. 8. Trainingestimators and selectors for making a selection decisions regardingtranscription units.

FIGS. 31-43, among others, describe various systems and methods that maybe used to generate higher accuracy transcriptions. In some embodiments,the higher accuracy transcriptions may be generated using the fusionconcepts discussed in FIGS. 13-17. Alternatively or additionally, thehigher accuracy transcriptions may be generated based on selectingtranscriptions from transcription units with higher accuracy. The higheraccuracy transcriptions may be used for training of ASR systems, forproviding to user devices, or monitoring CA activity, among other uses.

FIG. 31 illustrates another example environment 3100 for transcriptionof communications, in accordance with some embodiments of the presentdisclosure. The environment 3100 may be configured to generatetranscriptions of audio by first transcribing the audio with an ASRsystem 3120. The audio may also be broadcast to a CA by way of an audiointerface 3122. For example, the audio interface 3122 may be configuredto broadcast audio to a CA or provide the audio to a device associatedwith the CA that may broadcast the audio.

In some embodiments, the audio may be delayed before being provided tothe audio interface 3122 by a delay mechanism 3102. In some embodiments,a text editor 3126 may be configured to obtain the transcriptions fromthe ASR system 3120. The text editor 3126 may also be configured topresent the transcriptions to a CA or to provide the transcription to adevice for presentation to a CA. The text editor 3126 may obtain inputsfrom the CA regarding edits to the transcription. The text editor 3126may be configured to change the transcription to correct the errors. Insome embodiments, the audio interface 3122 and the text editor 3126 maybe part of a CA client discussed with respect to FIGS. 1 and 4, amongothers.

In some embodiments, the delay mechanism 3102 may add a delay to theaudio to make editing of the transcription easier for the CA. The delaymay be provided so that transcriptions appear at a desired point, suchas during, slightly before, or slightly after the corresponding audio.The delay value and whether delay is activated may depend on settingsdetermined by the CA, a CA supervisor, or an administrator. The delaymechanism 3102 may be configured to maintain a constant delay, oralternatively to vary a delay period. The delay period may be set inresponse to output from the ASR system 3120. For example, in conjunctionwith output text, the ASR system 3120 may provide endpoints that markthe time of the beginning and/or ending of each word. As recognizedwords are displayed to the CA, the delayed audio may be synchronized tothe display of the recognized words using the endpoints.

In some embodiments, the display of the text editor 3126 or audiosignals provided by the audio interface 3122 may be configured to drawthe CA's attention to areas most likely to need correction. For example,the display may indicate ASR system confidence via color coding,highlighting, changes in font, brightness changes, or by othervariations in the visual presentation.

To save typing or reduce errors, the ASR system 3120 may provide arecognized output such as an n-best list, WCN, or lattice to the texteditor 3126 so the text editor 3126 may present alternative words orphrases for the CA to select to be used in the output transcriptioninstead of words in the first hypothesis initially selected by the ASRsystem 3120. A portion of text may be displayed with a variation in thevisual presentation as described above, indicating that the text editorhas one or more alternate hypotheses available. Examples of how a texteditor may provide CA editing options may include:

-   -   1. The alternates may appear preemptively, showing up        spontaneously on the screen of the text editor in a pop-up or        drop-down menu. The CA may accept the first hypothesis, select        one of the alternates, or ignore the alternates and accept the        first hypothesis by default.    -   2. Alternates may appear when a CA hovers over a word or clicks        on a word.    -   3. Alternates may appear when a CA starts to edit, such as by        making changes to the text.    -   4. As the CA is making corrections or entering text, a predictor        using a rich ASR output, a lexicon or dictionary, a language        model, comparison of the transcription to previous        transcriptions, predictive typing, and other methods may predict        what the CA is about to enter and display it on the screen. The        CA may accept the prediction if it is correct.    -   5. As the CA makes corrections to a word or phrase, a first        hypothesis for words not being edited may change in response to        the CA entry. Because the text editor 3126 may have received        multiple hypotheses from the recognizer in the form of an n-best        list, WCN, lattice, or other rich output, the change may include        words before and/or after the point where the CA is making        corrections. For example, suppose the speaker says, “I'll meet        you at Monster Burger tonight,” but the ASR system 3120 may        transcribe the phrase as “I'll meet you at Mothers Diner        tonight.” The incorrect transcription may appear on the screen        of the text editor 3126, but as soon as the CA types three        letters “Mon” in correcting “Mothers,” the text editor 3126 may        recognize that the correct word must begin with “Mon,” search        for the next best hypothesis under that constraint and find        “Monster Burger.” Thus, two corrected words (“Monster Burger”)        may appear on the screen before the CA has even completed typing        one word. The predictor may use additional methods, such as        those listed in #4 above, to predict and correct text and to        provide suggestions to the CA.    -   6. When a CA clicks, taps, or otherwise selects a portion of        text (i.e. a first hypothesis) such as a word, the text editor        may replace the portion of text with an alternative such as the        second or next-best hypothesis from a transcription unit. If the        CA selects the text a second time, the text may be replaced with        a third hypothesis, and so on. Additionally or alternatively,        each left-click may replace a portion of text with the next        hypothesis and a right-click may enable other editing options.

In some embodiments, the environment 3100 may also be configured with aCA activity monitor 3104. In this and other embodiments disclosed hereinwhere an ASR system may provide transcriptions automatically and where aCA may be aware that the ASR system is miming, there is a risk that theCA may stop working or work at a reduced performance level.

In some embodiments, the CA activity monitor 3104 may be configured tomonitor the CA for unproductive behavior and advise the CA, the CA'ssupervisor, or otherwise provide feedback, reports or alarms so that thebehavior may be verified and/or corrected. In some embodiments, wheretext is produced by the ASR system 3120 without corresponding text fromthe CA, the text from the ASR system 3120 may appear in a differentcolor or font, highlighted, or otherwise marked so that the CA may moreeasily determine text to which the CA did not contribute. Additionallyor alternatively, the CA's supervisor, once alerted, may use remoteaccess software to further monitor the CA.

In some embodiments, the CA activity monitor 3104 may be configured toperiodically place pre-recorded test communication sessions to the CAwhere the transcription is known and where errors are inserted into thetranscription. If the CA fails to correct an acceptable number orpercentage of the errors, the CA activity monitor 3104 may signal poorCA performance.

Alternatively or additionally, the CA activity monitor 3104 may beconfigured to cause the text editor 3126 to present deliberate errors tothe CA that are not errors in the transcription output by the ASR system3120 during a communication session. For example, the transcription fromthe ASR system 3120 may be provided to a device for display to asubscriber and to the CA activity monitor 3104. The CA activity monitor3104 may select a word at random from the transcription output anddelete the word, replace the word with another word, or insert a word.The CA activity monitor 3104 may provide the transcription to the texteditor 3126 for presenting to the CA.

In some embodiments, the other word may be selected at random.Additionally or alternatively, a second ASR system or language model maybe used to construct errors that are believable, or relatively likelyaccording to an ASR system or language model, so that the CA does notdiscern that the errors are being input to a transcription. In someembodiments, the second ASR system may be configurable for variableaccuracy to adjust the number of constructed errors. If the CA fails tocorrect an error, or if the CA's error correction performance over timefalls below a selected threshold, the CA activity monitor 3104 maysignal poor CA performance.

In some embodiments, the CA activity monitor 3104 may be configured toanalyze a second reference transcription created by a second ASR system.If the transcription generated by the ASR system 3120 is notsignificantly closer to the second reference transcription after beingedited by the CA, then the CA activity monitor 3104 may signal poor CAperformance. Additionally or alternatively, if the CA corrects less thana selected number of errors over one or more periods of time, the CAactivity monitor 3104 may signal poor CA performance. The selectednumber of errors may be constant, or it may vary from communicationsession to communication session. The selected number of errors may beresponsive to estimated ASR system accuracy of the ASR system 3120.Estimated ASR system accuracy may include estimated accuracy during acurrent communication session or averaged across multiple communicationsessions. The CA activity monitor 3104 may also use the estimatedaccuracy of the ASR system 3120 alone or of the ASR system 3120 with CAedits in determining whether to signal poor CA performance. The CAactivity monitor 3104 may take into account use of the text editor 3126and/or the audio interface 3122 by the CA in evaluating CA behavior. Forexample, if a CA stops speaking or exhibits signs that might otherwisebe construed as distracted, but is actively editing text, the CAactivity monitor 3104 may use the editing activity to suppress adistracted CA signal.

In some embodiments, the CA activity monitor 3104 may use a video imageobtained, for example, from a camera configured to record the CA todetect suspect behavior. The camera may or may not be visible to the CA.The image may, for example, be analyzed automatically by imageprocessing software, by a remote supervisor, or a combination thereof,to detect conditions and events such as:

-   -   1. The CA's eyes closed.    -   2. The direction of the CA's gaze or an indication of whether        the CA appears to be paying attention to the task. For example,        if the CA does not appear to be looking at the CA screen, the CA        activity monitor may signal suspect CA behavior.    -   3. The CA's posture.    -   4. The CA's body position or orientation. For example, if the        CA's face is located or oriented so that it appears the CA is        not looking at or cannot see the display, the CA activity        monitor may signal possible lack of attention.    -   5. An indication of the CA's hand position. For example, if the        CA's hands do not appear to be at the keyboard or mouse or if        the CA's body position suggests that the CA's hands are likely        not at the keyboard/mouse, the CA activity monitor may signal        that the CA is not prepared to correct errors.    -   6. Lip motion. For example, if the CA's lips are moving but        audio is not detected or vice versa, the CA activity monitor may        signal a possible equipment failure. In another example, if        audio is detected and the CA's lips are not moving, the CA        activity monitor may signal a possible distraction.

In some embodiments, the video may be monitored continuously. In anotherexample, the video may be monitored intermittently. In anotherembodiment, the video camera may be non-functional or unused, butvisible to the CA. The camera may include a light to suggest video isbeing captured. Other events used by the CA activity monitor 3104 mayinclude:

-   -   7. The CA may have an option to indicate that he/she is unable        to listen to or revoice at least a segment of a communication        session by, for example, entering “inaudible,” “garbled,” “I        missed that part,” or “I need to take a break.” Frequency of        such indications exceeding a selected threshold may indicate        poor CA performance. Additionally or alternatively, this feature        may be used to indicate a poor network connection or other        system failure.    -   8. In some embodiments, the CA activity monitor may, in        detecting distracted CA behavior, compare a metric such as        deliberate errors corrected, missed errors, edit distance        between ASR transcriptions, number of errors corrected,        estimated accuracy of the ASR system 3120, presence of voices or        music, events detected in a video signal, and the frequency of        phrases labeled as unable to caption to a threshold. In an        alternative scenario, the metrics may be features, of which one        or more may be combined, such as using a machine learning        system, to detect poor CA performance.

When the CA activity monitor 3104 signals suspect CA behavior, a remotemonitoring system may provide a supervisor means to observe the CA. Thisobservation may be live or from one or more recordings and may includelistening to caller audio, listening to CA audio, observing part or allof the CA's screen, observing transcriptions, watching video from acamera, and examining time records, computer login records, and dooraccess logs. Additionally or alternatively, a recording/playback systemmay allow the supervisor to observe recorded activities of the suspectCA, such as at or before the CA activity monitor detected a suspectevent. For example, the supervisor may be able to watch recorded videoof the CA during the communication session containing the suspect eventor during other communication sessions. In one implementation the CA'slips may be at least partly obscured, for example either continuously orwhen the lips are in motion, in the video to protect privacy of thecommunication session. Additionally or alternatively, the text of theconversation may be obscured and the audio may be renderedunintelligible to protect privacy.

Modifications, additions, or omissions may be made to the environment3100 without departing from the scope of the present disclosure. Forexample, in some embodiments, the audio interface 3122 may be configuredto obtain revoiced audio from the CA. The revoiced audio may be providedto another ASR system. The output of the ASR system 3120 and the otherASR system may be fused. In these and other embodiments, the CA activitymonitor 3104 may listen to audio to detect distractions or other suspectbehavior. For example, if the CA activity monitor 3104 hears music, theCA speaking, or other audio while the audio does not include speech, orif the CA mutes his/her microphone, the CA activity monitor 3104 maysignal that the CA is distracted. If the CA activity monitor 3104 hearsmultiple voices or a voice that does not correspond to the CA's voice,it may signal that the CA may be distracted. In another example, the ASRsystem 3120 may include multiple ASR systems and a fuser that combinesoutputs of the multiple ASR systems.

FIGS. 32a and 32b illustrate example embodiments of transcription units,in accordance with some embodiments of the present disclosure. In someembodiments, the transcription units 3214 may be configured to generatetranscriptions using a combination of a speaker-dependent ASR system3221 and a speaker-independent ASR system 3220. The transcription units3214 may include an audio interface 3222 analogous to the audiointerface 3122 of FIG. 31. The audio interface 3222 may be configured toobtain audio and revoiced audio based on a CA speaking the words in theaudio. The audio interface 3222 may provide the revoiced audio to theASR system 3221. The ASR system 3220 may obtain the regular audio. Eachof the ASR system 3220 and the ASR system 3221 may generate atranscription.

Each of the transcription units 3214 may include a fuser 3224 that maybe configured to fuse the outputs of the ASR system 3221 and ASR system3220. In some embodiments, the fuser 3224 may make a decision to usetext from the ASR system 3221 and ASR system 3220 for each word, foreach subword unit, for each phrase, for a speaker turn, for a remainingportion of a session, or for an entire session. The decision may takeinto account voting, endpoints, word accuracy probabilities, alignment,silence detection, and information from a language model in making thedecision. When switching between transcriptions from the ASR system 3221and ASR system 3220, the fuser 3224 may be configured to synchronizetext so that words of the transcription remains in sequential order (seeFIG. 19).

Each of the transcription units 3214 may also include a text editor3226. In the first transcription unit 3214 a of FIG. 32a , the texteditor 3226 may obtain the output transcription of the ASR system 3221.In these and other embodiments, the text editor 3226 may makecorrections to the transcription from the ASR system 3221 before thetranscription is fused with the transcription from the ASR system 3220.In the second transcription unit 3214 b of FIG. 32b , the text editor3226 may be configured to make corrections to the transcription outputby the fuser 3224. The embodiment of FIG. 32a illustrates an arrangementwhere the first transcription unit 3214 a may be configured to hide thecorrections from the ASR system 3220 from the CA interfacing with thetext editor 3226. In these and other embodiments, hiding the correctionsmay give the CA more incentive to provide complete, correcttranscriptions.

The embodiment of FIG. 32b illustrates an arrangement where the secondtranscription unit 3214 b may be configured to give the CA finalauthority over the edits and may reduce the number of edits made by theCA.

Turning now to FIG. 32b , in some embodiments, the CA may stop revoicingthe audio. In these and other embodiments, the ASR system 3221 may stopgenerating a transcription. However, the ASR system 3220 may continue togenerate a transcription and the fuser 3224 may be configured to outputthe transcription from the ASR system 3220. In these and otherembodiments, the CA may go back and fix previous errors in thetranscription through the text editor 3226. For example, the text editor3226 may display a current and previous transcriptions where previoustranscriptions have already been provided to a user device forpresentation. Thus, the text editor 3226 may display more than just thecurrent transcription being generated.

In some embodiments, the transcriptions from the ASR system 3220 may bepresented to a CA. In these and other embodiments, the transcriptionsfrom the ASR system 3220 may be presented to the CA in a marked format,such as a different color, so that the CA can easily observe the actionof the ASR system 3220. While the CA is not revoicing, the fuser 3224may forward the transcription from the ASR system 3220 as-is, withoutcombining the transcriptions from the ASR system 3221 as there are notranscriptions from the ASR system 3221 without revoicing. Once the CAresumes revoicing, transcriptions from the ASR system 3221 may besynchronized with transcription from the ASR system 3220 and the fuser3224 may resume fusing the transcriptions.

In some embodiments, various inputs, each corresponding to the sameword, segment of the conversation, or point in time in the communicationsession audio stream, may arrive at the fuser 3224 input at differenttimes, due to variations in latency for the various transcription paths.For example, in some embodiments, the ASR system 3220 may be faster thanthe ASR system 3221 such that the transcription from the ASR system 3220may arrive at the fuser 3224 input ahead of the transcription from theASR system 3221. In these and other embodiments, the fuser 3224 maycreate and transmit a fused transcription for a given segment of theconversation after the fuser 3224 has received all inputs correspondingto the segment. Alternatively or additionally, the fuser 3224 maytransmit a fused transcription to a user device for presentation afterthe fuser 3224 has received one or more input transcriptions. Afterreceiving the other transcription, the fuser 3224 may determine acorrection to the previous transcription based on the othertranscription and provide the changes to a user device. Additionally oralternatively, the fuser 3224 may also change the fused transcriptionand transmit changes to the user device in response to changes in inputtranscriptions, such as if one of the ASR systems 3220 and 3221 revisesa previous hypothesis. In some embodiments, the fuser 3224's style orconfiguration may change in response to one or more factors such as achange in speech recognition accuracy of one of the ASR systems 3220 and3221, a change in CA behavior, or a change in values of one or morefeatures in Table 2 and Table 5. For example, in response to accuracy oftranscriptions from the ASR system 3220 being above a particularthreshold, the fuser 3224 may operate to provide the first transcriptionreceived and making corrections. In these and other embodiments, inresponse to the accuracy of transcriptions from the ASR system 3220being below a particular threshold, the fuser 3224 may wait for bothtranscriptions before providing a transcription. Modifications,additions, or omissions may be made to the transcription units 3214without departing from the scope of the present disclosure.

FIGS. 33a, 33b, and 33c are schematic block diagrams illustratingexample embodiments of transcription units, in accordance with someembodiments of the present disclosure. The FIGS. 33a, 33b, and 33cillustrate a transcription unit 3314. The transcription unit 3314 may beconfigured to operate in multiple different modes. FIG. 33a illustratesthe transcription unit 3314 operating in mode 1, FIG. 33b illustratesthe transcription unit 3314 operating in mode 2, and FIG. 33cillustrates the transcription unit 3314 operating in mode 3.

In FIG. 33a , illustrating operating mode 1, the transcription unit 3314may be configured to receive communication session audio at an audiointerface 3322 that may obtain revoiced audio based on the audio from aCA. The revoiced audio may be provided to a first ASR system 3320 whichmay be speaker-dependent. The first ASR system 3320 may generate arevoiced transcription. A text editor 3326 may be configured to receiveinput from the CA to correct errors in the revoiced transcription.

In FIG. 33b , illustrating operating mode 2, the transcription unit 3314may be configured to receive communication session audio at a second ASRsystem 3321, which may be speaker-independent, and the audio interface3322 simultaneously. The audio interface 3322 may obtain revoiced audiobased on the audio from a CA. The first ASR system 3320 may generate afirst transcription. The second ASR system 3321 may generate a secondtranscription. The two transcriptions may be fused by a fuser 3306 andthe fused transcription may be sent to the text editor 3226. The texteditor 3326 may output the fused transcription for presentation to theCA and may be configured to receive input from the CA to correct errorsin the fused transcription.

In FIG. 33c , illustrating operating mode 3, the transcription unit 3314may be configured to receive communication session audio at the secondASR system 3321 and the audio interface 3322 simultaneously or the audiointerface 3322 may receive delayed audio from an audio delay 3330 thatmay delay the communication session audio. The audio interface 322 maybroadcast the audio to the CA. The second ASR system 3321 may generate anon-revoiced transcription. The non-revoiced transcription may be sentto the text editor 3226. The text editor 3326 may output thenon-revoiced transcription for presentation to the CA and may beconfigured to receive input from the CA to correct errors in thenon-revoiced transcription.

In some embodiments, the third operating mode may be configured torelieve the CA from revoicing the audio such that the CA may attend tocorrecting errors in the transcriptions using the text editor 3326.

In some embodiments, the delay of the audio performed by the audio delay3330 may be constant or variable. In a first example, audio delay may beconstant and may be responsive to estimated latency of a transcriptionprocess of the audio as illustrated in FIG. 33c . For example, thelatency from when the audio is received by the second ASR system 3321and when the audio is provided on the text editor 3326. In a secondexample, the delay performed by the audio delay 3330 may be responsiveto the time of appearance for words in the text editor 3326, compared tothe time of appearance for words in the communication session audio,which may be estimated using endpoints from the second ASR system 3321.The delay performed by the audio delay 3330 may then be adjusted so thataudio is presented by the audio interface 3322 substantially synchronouswith the appearance of the corresponding text in the text editor 3326.Additionally or alternatively, a further delay adjustment may be appliedso that text appears in the text editor 3326 a selected amount of timebefore or after the corresponding audio is broadcast by the audiointerface 3322.

In some embodiments, the transcription unit 3314 may be reconfigured,for example between modes 1, 2, and 3, using one of several methods,including:

-   -   1. If the CA stops speaking, the transcription unit may switch        to mode 3. When the CA begins speaking again, the transcription        unit may switch to mode 1 or 2.    -   2. The CA may take action, such as speaking a voice command,        pressing a key or foot switch, touching a region on a        touchscreen, or clicking a mouse, to switch between modes.    -   3. The switch between modes may be accomplished within the        implementation illustrated in FIG. 33b by action from the fuser        3306 to reduce errors. For example, if the fuser 3306 detects        that the CA is silent (such as when the CA is correcting errors)        or that the error rate of the first ASR system 3320 is above a        threshold, the fuser 3306 may select text from the second ASR        system to use for transcriptions, in which case the behavior of        the configuration of FIG. 33b may match that of mode 3. Where        the fuser 3306 detects that the first ASR system 3320 is        providing transcriptions, the fuser 3306 may select words from        the revoiced transcription, in which case the behavior of the        configuration of FIG. 33b may match that of mode 1.    -   4. A selector may direct transcription unit 3314 to switch        between modes in response to features such as items in Table 2        and Table 5. For example, a selector may determine that a        revoiced transcription unit is performing sufficiently well        without ASR2 and switch to mode 1. Additionally or        alternatively, a selector may determine that audio        characteristics and the CA skillset suggest that mode 3 will        provide the best transcription and switch to mode 3.

The latency, in some embodiments, for the various modes may bedifferent, so a synchronizer 3302 may buffer text as necessary andensure that there are no gaps or overlaps in the output transcriptionwhen the transcription unit 3314 switches between modes. Thesynchronizer 3302 is illustrated coming before the text editor 3326,though the synchronizer 3302 may alternatively come after (see FIG. 19).

As described above, the transcription unit 3314 may be configured tocontinue to provide transcriptions in real-time or near real-time to auser device regardless of actions by the CA. Thus, the CA stoppingrevoicing, editing, or other actions may not stop the transcription unit3314 from providing transcriptions to the user device.

In some embodiments, the transcription unit 3314 may include a bypass3304 for sending a transcription to a user device and bypassingproviding the text to the text editor 3326 for editing. In these andother embodiments, the bypass 3304 may be configured to forward textdirectly to a user device, before the text is presented to the CA forediting and/or before receiving input from the CA regarding edits to thetext by the text editor 3326. In these and other embodiments, after thetext editor 3326 receives a correction (e.g., edit, insertion, ordeletion), the corrected text may be forwarded to the user. The userdevice may then display the corrections. The corrections may bepresented in an altered form such as in highlighted text so that thereader can see which text has been corrected. In some embodiments, thebypass 3304 may be configured to operate in response to a CA activitymonitor determining that the CA has stopped editing the text.

Modifications, additions, or omissions may be made to the transcriptionunits 3314 without departing from the scope of the present disclosure.For example, in some embodiments, the transcription units 3314 may notinclude the bypass 3304. In these and other embodiments, the text editor3326 may be configured to forward text directly to a user device, beforethe text is presented to the CA for editing and/or before receivinginput from the CA regarding edits to the text. In these and otherembodiments, after the text editors 3326 receives a correction (e.g.,edit, insertion, or deletion), the corrected text may be forwarded tothe user. The user device may then display the corrections. Thecorrections may be presented in a form such as highlighted text so thatthe reader can see which text has been corrected.

FIG. 34 is another example embodiment of a transcription unit 3414, inaccordance with some embodiments of the present disclosure. In someembodiments, the transcription unit 3414 may be configured to receivecommunication session audio at a second ASR system 3421, which may bespeaker-independent, and an audio interface 3422. The audio interface3422 may obtain revoiced audio based on the communication session audiofrom a CA. A first ASR system 3420 may generate a first transcriptionbased on the communication session audio. The second ASR system 3421 mayobtain the revoiced audio and generate a second transcription based onthe revoiced audio. The two transcriptions may be fused by a fuser 3424and the fused transcription may be sent to the text editor 3426. Thetext editor 3426 may output the fused transcription for presentation tothe CA and may be configured to receive input from the CA to correcterrors in the fused transcription.

In some embodiments, the transcription unit 3414 may further include aCA activity monitor 3406 and silence detector 3402. The CA activitymonitor 3406 may be an example of an alternative embodiment of the CAactivity monitor 3104 of FIG. 31.

In some embodiments, the silence detector 3402 may be configured tomonitor one or more of: the communication session audio and the revoicedaudio. In some embodiments, the silence detector 3402 may be configuredto detect silence of the communication session audio or the revoicedaudio and assign a silence certainty rating based on an average energyof the communication session audio or the revoiced audio falling below aselected threshold. The silence detector 3402 may provide an indicationregarding silence in communication session audio and the revoiced audioto the CA activity monitor 3406.

The CA activity monitor 3406 may be configured to compare silencedetector indications regarding the communication session audio and therevoiced audio, taking into account a delay, to determine if the CAshould be speaking when the CA is not speaking/revoicing. For example,if speech is detected from the communication session audio, but not theCA, taking into account a time lag between the communication sessionaudio and the revoiced audio, at a high confidence and for a significantperiod of time, the CA may be determined to have stopped speaking andthe CA activity monitor 3406 may signal poor CA performance.

In some embodiments, the CA activity monitor 3406 may also receive inputfrom one or more of: the first transcription, the second transcription,the fused transcription, the edited transcription, and log activity ofthe text editor 3426. Additionally or alternatively, the CA activitymonitor 3406 may make a determination of whether the CA is performing asexpected based on the input. For example, the CA activity monitor 3406may compare text output from the fuser 3424 with text output from thetext editor 3426 and determine that the CA is not appropriately makingcorrections. As another example, the CA activity monitor 3406 may usesilence information and a log of activity from the text editor 3426 todetermine that the CA has stopped revoicing. However, the CA activitymonitor 3406 may also take into account an indication of activity fromthe text editor 3426 suggesting that the CA has legitimately stoppedspeaking in order to edit text.

In some embodiments, the CA activity monitor 3406 may be configured todetect CA silence by finding sequences of words in the firsttranscription from revoiced audio that are missing in the secondtranscription of the communication session audio. The silence detector3402 may assign a higher silence certainty when the sequence of wordsmissing in the first transcription from revoiced audio is followed by asequence of words that match a similar sequence in the secondtranscription of the communication session audio.

In some embodiments, the CA activity monitor 3406 may be configured todetect CA silence by comparing the first transcription from revoicedaudio with the second transcription of the communication session audio.For example, if the second transcription includes an amount of text thatis more than what the second transcription includes for a thresholdamount of time or over a threshold number of words or characters, the CAmay be determined to have slowed or stopped working and the CA activitymonitor 3406 may signal distracted CA performance. In these and otherembodiments, the CA activity monitor 3406 may take into account a timelag between the communication session audio and the revoiced audio.

In some embodiments, the CA activity monitor 3406 may be configured todetect CA silence by comparing the number of words or subword units suchas syllables in the revoiced audio over a particular period of time tothe number of words or subword units in the communication session audioover the particular period of time. The number of words may, forexample, be counted in the first and second transcriptions.Alternatively or additionally, the number of subword units may becounted by analyzing audio to determine the number of changes infeatures such as volume and/or the frequency spectrum over a selectedperiod of time. For example, when a metric measuring a change in themagnitude spectrum exceeds a specified threshold, a new subword unit maybe considered to have begun.

In some embodiments, the transcription unit 3414 may also be configuredto assist the CA. For example, the second transcription may be providedto a user device after the second ASR system 3421 generates the secondtranscription. However, the text editor 3426 may not present the secondtranscription for a fixed or variable length of time or until after theCA revoices the corresponding audio. Delaying the presentation of thesecond transcription may encourage the CA to continue speaking and mayhelp avoid confusing the CA when seeing transcriptions before the CArevoices the corresponding audio.

In some embodiments, an accuracy estimator may measure the overall errorrate of the transcription unit 3414 and may use the error rate tomeasure performance of the CA. The error rate may also be used toprovide feedback or other incentives to the CA to raise the combinedsystem accuracy. The CA activity monitor 3406 may provide the error rateto the text editor 3426 or other system that may present the error rateor another performance metric during or at the end of a communicationsession. For example, the another performance metric may include afunction of the estimated CA accuracy in revoicing the audio and afunction responsive to the estimated difficulty in revoicing thecommunication session audio. In another example, the performance metricmay include a function of the estimated accuracy of an ASR systemreceiving the revoiced audio and the estimated accuracy of an ASR systemreceiving the communication session audio.

Modifications, additions, or omissions may be made to the transcriptionunits 3414 without departing from the scope of the present disclosure.For example, in some embodiments, the transcription units 3414 mayinclude a synchronizer and/or audio delay. In some embodiments, an audiodelay may delay audio so that the CA hears it at approximately the sametime as the corresponding text appears in the text editor 3426. In afirst example, the audio delay time may be set to zero. In a secondexample, the audio delay time may respond to word endpoints for one ormore words from the second transcription compared to the point in timewhere the one or more words appear in the text editor 3426. In a thirdexample, the audio delay may respond to communication session audio,presentation of text from the text editor 3426, and an arrival time ofthe text at the text editor 3426.

FIG. 35 is a schematic block diagram illustrating an example environment3500 for editing by a CA 3518, in accordance with some embodiments ofthe present disclosure. In some embodiments, the CA 3518 may monitormultiple audio signals, such as multiple communication sessions,simultaneously and make corrections to transcriptions of the audiosignals as needed. FIG. 35 illustrates audio signals 1-4. Each of theaudio signals 1-4 are provided to one of four ASR systems, 3520 a-3520d, collectively the ASR systems 3520. The ASR systems 3520 may generatetranscriptions of the audio signals 1-4 and provide the transcriptionsto an editor 3502. The editor 3502 may buffer the text, segment wordsinto phrases, and deliver transcriptions to the respective user devicesto be displayed.

In some embodiments, the CA 3518 may listen to the communicationsessions and view the transcriptions on one or more displays 3504. Theeditor 3502 may be configured to receive input form the CA 3518 to makecorrections to the transcriptions. If the CA 3518 makes correctionsafter the transcriptions have been sent to a user device, the editor3502 may be configured to send the corrections to the user device.

In some embodiments, a set of loudspeakers 3506 a-d are configured tobroadcast the audio signals 1-4. Additionally or alternatively, one ormore loudspeakers 3506 may be configured to broadcast audio frommultiple audio signals with per-audio stream signal variations so thatthe apparent position of each audio signal, from the CA's 3518perspective, appears at various locations in space. The location inspace may be set, for example, by adjusting loudness, phase, delay, andother characteristics of the audio signals. For example, twoloudspeakers 3506 a and 3506 b may play audio from all the audiosignals. In these and other embodiments, each audio signal may bepositioned in space within the stereo image by adjusting the relativeloudness of each audio signal sent to each loudspeaker. Additionally oralternatively, the audio signals may be broadcast to the CA 3518 viastereo headphones and the audio signal to the headphones may be adjustedso that each audio signal appears to come from a different location inspace. In some embodiments, the CA 3518 may rewind and replay portionsof one or more conversations. The replay may use a speech rate changerto play speech at a rate faster or slower than real-time and/or removeor shorten silent segments. Modifications, additions, or omissions maybe made to the environment 3500 without departing from the scope of thepresent disclosure. For example, the number of audio signals and ASRsystems may be more or less than four.

FIG. 36 is a schematic block diagram illustrating an example environment3600 for sharing audio among CA clients 3622, in accordance with someembodiments of the present disclosure. In the environment 3600, fouraudio signals 1-4 are received. The audio signals may be from differentor the same communication sessions or from other sources. Atranscription for each of the audio signals 1-4 is generated by aseparate one of four ASR systems 3602 a-d, collectively ASR systems3620, and sent to a separate one of four transcription managers 3602a-d, collectively transcription managers 3602.

In some embodiments, the environment 3600 may also include fourestimators 3604 a-d, collectively the estimators 3604, associated witheach of the ASR systems 3620. The ASR systems 3620 may provide thetranscriptions to the estimators 3604. The estimators 3604 may beconfigured to determine a characteristic of at least a segment of thetranscriptions received. A segment may be a word, a few words, or aspeaker turn delimited by silence or by the other party speaking. Insome embodiments, the characteristic may be an accuracy of thetranscription or other characteristic of a transcription that may bemeasured as described in this disclosure.

In some embodiments, the estimators 3604 may provide the characteristicof the transcriptions to the transcription managers 3602. Thetranscription managers 3602 may be configured to compare thecharacteristic to a threshold. The threshold may be based on the sourceof the audio. For example, the threshold may be based on the type of thecommunication session as described in this disclosure. In response tothe characteristic satisfying a threshold, the transcription may beprovided to a user device associated with the audio signal correspondingto the transcription. In response to the characteristic not satisfyingthe threshold, the segment and the audio corresponding to the segmentmay be provided to the automatic communication session distributor(“ACD”) 3606. Alternatively or additionally, in response to thecharacteristic not satisfying the threshold, the segment and the audiocorresponding to the segment may be provided to the user device as well.

The ACD 3606 may receive a segment and audio from the transcriptionmanagers 3602 and route it to an available CA client 3622. The CA client3622 may be configured to present the audio and the segment to a CAassociated with the CA client 3622 and obtain input from the CAregarding edits to the segment. In some embodiments, the CA may not editthe segment. The ACD 3606 may provide the edits back to thetranscription manager 3602. The transcription manager 3602 may providethe edited segment to a user device for presentation. Alternatively oradditionally, the transcription manager 3602 may provide the edits ascorrections to a user device that previously obtained the segment.

In some embodiments, although not depicted here, the CA clients 3622 maybe part of transcription units associated with the ASR systems 3620. Inthese and other embodiments, the transcription units may be utilized toaccurately and efficiently generate corrections to the segment byincluding a speaker-dependent ASR, a companion ASR system and fuser, orother forms of ASR systems.

In some embodiments, the CA clients 3622 may present text that the ASRsystems 3620 transcribed before, during, and, if it is available, afterthe segment. In some embodiments, the CA client 3622 may obtain inputsfrom a keyboard, mouse, voice commands, revoicing, or other forms ofcomputer input.

In some embodiments, to help CAs associated with the CA clients 3622understand the context or topic of conversation, the ACD 3606 may beconfigured to use the same CA client 3622 or group of CA clients 3622for a given communication session. In these and other embodiments, theACD 3606 may provide one of the CA clients 3622 with a transcription orsummary of prior portions of the conversation.

In some embodiments, the ACD 3606 may be configured to favor selecting aCA client 3622 for a communication session based on the CA client 3622previously handling audio associated with one or more devicesparticipating in the communication session. In some embodiments, the ACD3606 may select CA clients 3622 based on information regarding the CAsassociated with the CA clients 3622, such as a skill level of a CA, idletime for CA, status of a CA such as seniority or performance, experiencewith a given language or accent, ability to handle particularcharacteristics such as high noise levels, or knowledge such as medicalor legal expertise related to the current conversation topic.

In some embodiments, the characteristic determined by the estimator 3604may be a predicted accuracy of a segment. The predicted accuracy may bethe accuracy of the segment before the segment has been partly or fullytranscribed. In these and other embodiments, the predicted accuracy maybe based on an estimated accuracy of past speech transcriptions orsegments thereof. In response to the predicted accuracy/quality notsatisfying the threshold, the segment may be sent to a CA client 3622.In these and other embodiments, a transcription unit that includes theCA client 3622 may be configured to transcribe the segment atoverlapping time periods as the segment is transcribed using the ASRsystems 3620. By streaming segments to CA clients 3622 based onpredicted accuracy, round-trip latency to and from the CA clients 3622may be reduced. In these and other embodiments, segments may continue tostream to the CA clients 3622 until the predicted accuracy rises abovethe threshold. Modifications, additions, or omissions may be made to theenvironment 3600 without departing from the scope of the presentdisclosure. For example, the number of audio signals and associatedelements (e.g. ASR systems and transcription managers) and the number ofCA clients may be more or less than the numbers illustrated.

FIG. 37 is a schematic block diagram illustrating an exampletranscription unit 3714, in accordance with some embodiments of thepresent disclosure. In some embodiments, the transcription unit 3714 maybe configured with seven ASR systems 3720 a-g, collectively, ASR systems3720. An audio interface 3718 may be configured to provide audioreceived by the transcription unit 3714 to a CA and generate revoicedaudio based on speech from the CA. The audio interface 3718 may providethe revoiced audio to ASR systems 3720 a-d. The ASR system 3720 d may bea speaker-dependent ASR system that has been trained on the voice of theCA. In these and other embodiments, the ASR system 3720 d may use a CAprofile 3722 associated with the CA that may include an ASR model andother models. The ASR systems 3720 e-g may be configured to receive theaudio without revoicing. Each of the ASR systems 3720 a-g may generate atranscription that may be provided to a fuser 3724. Although depictedhere with seven total ASR systems, it is contemplated that anyreasonable number of ASRs may be used here or in any of the describedembodiments of the present disclosure. Similarly, any reasonable numberof audio interfaces, speaker-dependent ASR systems, and text editors maybe utilized.

In some embodiments, the fuser 3724 may be configured to receive thetranscriptions from the ASR systems 3720. The fuser 3724 may combine thetranscriptions to generate fused transcription. In some embodiments, theASR systems 3720 a-c and 3720 e-g may be any combination ofspeaker-independent or speaker-dependent ASR systems. Alternatively oradditionally, the ASR systems 3720 a-c and 370 e-g may each beconfigured in any number of ways as described in this disclosure. Thenumber of ASR systems and the number of transcriptions provided to thefuser 3724 may be more or less than the numbers illustrated. Forexample, in some embodiments, the transcription unit 3714 may includetwo, three, four, or five ASR systems besides the ASR system 3720 d. Inthese and other embodiments, one or more of the ASR systems may use therevoiced or non-revoiced audio to generate the transcriptions.Alternatively or additionally, one or more of the ASR systems may run ina reduced or crippled state. Alternatively or additionally, one or moreof the ASR systems may be configured as indicated in the below list.

-   -   1. An ASR system where the acoustic model is trained on speech        collected from multiple callers.    -   2. An ASR system where the acoustic model is trained on speech        collected from multiple CAs.        -   a. In some embodiments, multiple CAs are predominantly male.            Alternatively, the model may be built using multiple male            and female voice samples, then adapted to multiple            predominantly male voices. This model may be used when it is            determined that a CA providing revoiced audio is male.        -   b. Additionally or alternatively, the multiple CAs are            predominantly female. Alternatively, the model may be built            using multiple male and female voice samples, then adapted            to multiple predominantly female voices. This model may be            used when it is determined that a CA providing revoiced            audio is female.    -   3. An ASR system where the acoustic model is trained on speech        collected from multiple callers and multiple CAs.    -   4. An ASR system trained on speech collected from female CAs and        an ASR system trained on speech collected from male CAs.    -   5. An ASR system trained on speech collected from CAs with        demographics similar to that of a CA providing revoiced audio.        Demographics may include one or more of gender, spoken language,        accent, geographic region, area code of cell phone, age, and        membership in a cluster where CAs are divided into groups using        a clustering method such as k-means.    -   6. An ASR system using one or more models selected to increase        performance for a CA providing revoiced audio. For example, if        the highest ASR accuracy is obtained with an acoustic model        trained on CAs from a particular geographic location, then an        acoustic model trained on speech from that location may be used.    -   7. An ASR system using one or more models selected to increase        performance on the current communication session. For example,        if the communication session topic pertains to setting up an        appointment, then a language model trained on communication        sessions where people make appointments may be used by one or        more of the ASR systems. Additionally or alternatively, the ASR        systems may be configured or selected in response to audio or        speaker characteristics such as communication session volume,        noise level, speaker clarity, speaker demographic (e.g. age,        gender, status as a child or minor, accent, speech or hearing        impairment, etc.), or information from previous communication        sessions including the speaker.    -   8. A first ASR system trained on communication session data from        the captioning service and a second ASR system trained on data        outside the captioning service.

Additionally or alternatively, an ASR system running on a deviceproviding the audio to the transcription unit 3714 or another device maybe used to create a transcription. In these and other embodiments, thetranscription may be used alone, it may be fused with transcriptionsfrom one or more ASR systems 3720, or it may be used in multipleconfigurations at different times in response to estimated accuracy,difficulty of transcribing a given audio stream, network connectivity,availability of transcription units, and other factors such as thefeatures listed in Table 2 and Table 5.

In some embodiments, the transcription unit 3714 may include a firsttext editor 3726 a and a second text editor 3726 b. The first texteditor 3726 a may be configured to display the transcription from theASR system 3720 d and obtain edits from a CA for the transcription fromthe ASR system 3720 d. The second text editor 3726 b may be configuredto display the fused transcription and obtain edits from a CA for thefused transcription. The CA that may use the first and second texteditors 3726 a and 3726 b may be the same or different.

In some embodiments, the bandwidth and sample resolution of the inputsignals to the ASR systems 3720 may be different and may vary, dependingon the communication session. For example, when the audio passes througha telephone network, the audio may be sampled at 8 kHz with a resolutionof eight bits with μ-Law encoding. In response to this encoding, one ormore of the ASR systems 3720, for example the ASR systems 3720 e-g, mayrun models trained using input audio sampled at 8 kHz with μ-Lawencoding. In these and other embodiments, the ASR systems 3720 a-d thatmay obtain the revoiced audio may use models trained on speech sampledat a higher sampling rate, such as at 16 kHz, and at a higher samplingresolution, such as 16-bit linear. In some embodiments, audio from acommunication network may use other sampling and encoding methods suchas a 16 kHz sampling rate, a 16-bit sample encoding, wideband audio,wideband voice, ITU standard G.722, HD Voice, MP3, AMR-WB, codecs usedfor VoIP and videoconferencing, etc. In these and other embodiments, adetermination may be made regarding the audio quality and one or moreASR systems 3720 may be configured in response to the determined audioquality. Additionally or alternatively, a first one of the ASR systems3720 may be configured for audio sampled and encoded in a first formatand a second one of the ASR systems 3720 may be configured for audiosampled and encoded in a second format. Additionally or alternatively,audio in a first format may be converted to a second format andpresented to an ASR system 3720 configured for the second format. Forexample, wideband audio may be downsampled to 8 kHz and processed by anASR system 3720 configured to recognize 8 kHz speech.

In some embodiments, audio may be transmitted to the audio interface3718 and the ASR systems 3720 e-g substantially at the same qualitylevel in which it is received. Additionally or alternatively, audio maybe processed by speech enhancer 3702 a-d, collectively speech enhancers3702, that may be configured to improve performance of the ASR systems3720. The speech enhancers 3702 may be configured to perform one or moreof the following:

-   -   1. Noise reduction.    -   2. Bandwidth extension. For example, a 4 kHz bandwidth telephone        signal may be converted to a wideband (e.g., 8 kHz) signal so it        is easier for the ASR systems 3720 to understand.    -   3. Spectral filtering.    -   4. Loudness compression or automatic gain control (i.e.,        increasing loudness of quiet segments relative to that of loud        segments). Additionally or alternatively, the speech enhancers        3702 may increase the gain for quiet speakers relative to loud        speakers.    -   5. Non-uniform or varying amplification such as amplifying        consonants more than vowels.    -   6. Processing speech to make it more intelligible.    -   7. Speech normalization, which is transforming a speaker's voice        quality to a voice quality more similar to a selected group of        speakers. Transformation may include accent reduction, gender        normalization, or removal or alteration of other speaker        characteristics.    -   8. Altering the audio characteristics of one party's voice, such        as the subscriber's voice, compared to another party's voice        such as the transcription party's voice, or vice-versa, so that        the CA has an audible indication of which party is speaking. For        example, a tone or other audio marker may be added to one        communication session audio signal (such as the subscriber's        audio signal) or at the point where the subscriber stops        speaking and the transcription party starts speaking and vice        versa. In some embodiments, the audio indication may appear in        only one ear of a CA headset. Additionally or alternatively, one        party's audio signal may appear with echo, reverberation,        distortion, altered pitch, or spectral shaping.    -   9. Altering speech rate. This may include (a) slowing down        speech, (b) speeding up speech or reducing the duration of        silence portions, (c) slowing down speech segments where the        speaker is talking quickly and speeding up segments where the        speaker is talking slowly, and (d) varying speech rate        dynamically so that it is easier to understand or easier to        transcribe. In some embodiments, speech may be slowed down when        a CA begins to fall behind so that the audio is more closely        aligned with the point in time where the CA is revoicing and        then speeding up the signal (including cutting silence) at a        later point in time to catch up to real-time. Alternatively or        additionally, altering the speech rate may include skipping part        of the audio played to a CA when the speech enhancer 3702        detects that a CA is behind and inserting text into the        transcription that is generated by an ASR system processing the        skipped audio.    -   10. Separating a signal with multiple voices into a multichannel        signal where various voices are placed at different points in a        sound field (see FIG. 35) or directed to different ASR systems        3720. This way, a CA may more easily discern who is speaking by        their apparent location. The multiple voices may be from        multiple speakers using the same calling device such as a group        of people using the transcription party device, or it may be        speakers on separate lines such as the different parties of a        communication session.    -   11. Noise cancelling for ambient audio interference presented to        a first CA based on a signal collected by audio interface 3718        for the first CA and/or for at least a second CA. In some        embodiments, one or more signals may be captured by a microphone        used by the first CA and/or by microphones used by one or more        CAs in physical proximity to the first CA. Additionally or        alternatively, signals may be collected by additional        microphones, such as microphones attached to a CA headset. The        signal or signals may be processed by one or more adaptive        filters, combined, inverted, and broadcast to the first CA to        cancel ambient interference arriving at the first CA's ears via        an acoustic path (e.g. through the air). Additionally or        alternatively, the audio interface 3718 may include noise        cancelling headphones.

In some embodiments, when the audio is part of a communication session,the speech enhancers 3702 may use a number of different methods toautomatically determine which party of the communication session (e.g.,the subscriber who may be hearing-impaired, and the transcription party,who may also be hearing-impaired) is speaking, including comparing therelative energy levels of the subscriber's audio signal and thetranscription party's audio signal, and using voiceprints to distinguishbetween voices on the same channel. For example, on a two-waycommunication session or conference communication session, the audio ofthe speaking party may be identified for the CA using visual and/oraudio indicators. Additionally or alternatively, the transcription unit3714 may also send indicators to a user device so that the user devicecan display speaker information such as “New speaker:” or “FemaleSpeaker:” or “Party 3:” to the subscriber. Such indicators may also besent to the CA by, for example, providing a panel light, a lit region onthe CA screen, or a displayed text advisement to notify the CA whichspeaker is talking and when the speaker changes. The visual indicatormay be a signal for the CA to resume revoicing. For example, a region ofthe screen may dim or change color when the subscriber is speaking, andit is unnecessary for the CA to revoice the audio, then brighten whenthe user speaks for which a transcription may be generated. While aparty is speaking that the CA may not revoice, one or more of the ASRsystems 3720 may caption the party and display a transcription orsummary to the CA so that the CA is updated on the conversation context.

In some embodiments, during the time when a CA is not revoicing orproviding input to correct a transcription, the CA client software maygive the CA alternate tasks to perform. For example, a group of one ormore transcription units may provide multiple services such as:

-   -   1. transcribing communication sessions for communication        devices;    -   2. transcribing recorded audio such as lectures, phone        communication sessions, and medical or legal records;    -   3. transcribing audio from a video;    -   4. transcribing conference communication sessions;    -   5. labeling data such as training data for training ASR and        other models;    -   6. labeling or analyzing data for contact center or call center        analytics;    -   7. initiating telemarketing communication sessions;    -   8. receiving customer support communication sessions where the        CA talks to the caller;    -   9. performing data entry;    -   10. language translation;    -   11. phone surveys;    -   12. generating data;    -   13. selling or providing customer support for a captioning        service;    -   14. recording audio data, for example, by reading a script aloud        and recording the voice sample; or    -   15. A CA revoices audio data as the CA's voice sample is        recorded.

One implementation of the last item (recording revoiced audio) mayinclude the steps of:

-   -   1. A set of one or more audio samples may be created. The audio        may be obtained from recruited subjects speaking according to a        set of instructions, audio generated by a text-to-speech        synthesizer, recordings from callers using a transcription        service, or voice samples collected from users of another type        of service.    -   2. In one scenario, an audio sample may be transcribed to create        a corresponding transcription after it is recorded. In another        scenario, a human reader or a text-to-speech synthesizer may        read a transcription to create a corresponding audio sample.    -   3. A check may be made to determine if a CA is available. When a        CA is available, a first audio sample may be played to the CA        for revoicing.    -   4. The CA may revoice the first audio sample to create a second        audio sample.    -   5. The second audio sample may be recorded.    -   6. The second audio sample may optionally be used to test CA        performance or to provide a transcription service such as a        transcription service.    -   7. The second audio sample and a corresponding transcription,        optionally in combination with other audio samples and        transcriptions similarly obtained from other CAs, may be used to        train one or more models such as an acoustic model or        punctuation model.    -   8. A model built from the second audio sample may be used by an        ASR system to recognize speech from one or more CAs, including a        CA other than the one who provided the second audio sample.

The method of using revoiced audio from a CA to train models may becombined with CA accuracy testing or another quality assurance process,including methods for testing CAs described herein so that the sameactivity (e.g., the CA speaking) may serve multiple purposes. Forexample, the results (creating a voice sample and a transcription) fromplaying an audio sample to a CA may be used both for training models andfor testing accuracy. When a CA is working on alternate tasks, a visualdisplay related to the alternate task may obscure at least part of thecaptioning screen when active, then it may disappear when it is time forthe CA to resume captioning.

Modifications, additions, or omissions may be made to the transcriptionunit 3714 without departing from the scope of the present disclosure.For example, the transcription unit 3714 may include a single speechenhancer 3702 that may provide audio to the audio interface 3718 and theASR systems 3720 e-g. Additionally or alternatively, the speechenhancers 3702 may be provided for the non-revoicing ASR systems but notfor the revoicing ASR systems.

FIG. 38 illustrates another example transcription unit 3814, inaccordance with some embodiments of the present disclosure. In someembodiments, an audio interface 3818 may obtain audio, provide the audioto a CA, and obtain revoiced audio. The revoiced audio may be providedto the ASR system 3820 a. The ASR system 3820 a may be aspeaker-dependent ASR system with respect to the CA and configured togenerate a revoiced transcription. The revoiced transcription may beprovided to a text editor 3826. The text editor 3826 may obtain editsfrom the CA and apply the edits to the revoiced transcription. Theoutput of the text editor 3826 may be provided to a scorer 3816.Alternatively or additionally, the output of the ASR system 3820 a maybe provided to the scorer 3816 and a second fuser 3824 b and notprovided to the text editor 3826.

The audio may also be provided to ASR systems 3820 b-d, which each maybe configured to generate a transcription. The transcriptions may beprovided to a first fuser 3824 a and the second fuser 3824 b, referredto collectively as the fusers 3824. The fusers 3824 may be configured togenerate fused transcriptions based on the received transcriptions. Insome embodiments, the output of the first fuser 3824 a may be providedto the scorer 3816. In these and other embodiments, the output of thesecond fuser 3824 b may be provided as the output transcription of thetranscription unit 3814. Alternatively or additionally, the output ofthe text editor 3826 may be provided as the output transcription of thetranscription unit 3814 and the transcription unit 3814 may not includethe second fuser 3824 b.

The transcription generated by the ASR system 3820 a and the output ofthe first fuser 3824 a may be compared by the scorer 3816. Since thefused transcription may contain errors, the scorer 3816 may use otherfeatures or provide a constant correction factor as discussed withrespect to FIG. 23. The scorer 3816 may determine an estimated accuracy,error rate, or other performance metric for the ASR system 3820 a.

In some embodiments, the output of the scorer 3816 may be provided asfeedback 3802 to the CA. Additionally or alternatively, outputs of thescorer 3816 may be incorporated into reports, messages to CA management,and processes to improve transcription unit selection methods or methodsto select between using revoiced or non-revoiced audio. Multiple typesof reports and alerts may be generated. For example, a first report maybe created to provide feedback to a CA for information and learningpurposes and a second report may be created that may affect the CA'semployment, compensation, or status. In these and other embodiments, theoutput of the scorer 3816 may be used for one or more of the exampleslisted in Table 13.

TABLE 13 1. Provide feedback to the CA. This may be immediate, such aswith a warning that the transcription may be incorrect, or it may becompiled into a periodic report. 2. If the reference disagrees with theCA, it may warn the CA about a suspect word or phrase. 3. The feedbackstep may create a pop-up to alert the CA of a potential error, providealternatives derived from an ASR n-best list, WCN, or lattice, and allowthe CA to ignore the alert, select one of the alternatives, or enter newtext. 4. The feedback step may highlight or otherwise mark suspect wordsor phrases and allow the CA to make a correction. If the CA clicks orhovers over a marked word, alternative suggestions may appeal'. 5.Feedback may appear as annotations in the text editor 3826.

Modifications, additions, or omissions may be made to the transcriptionunit 3814 without departing from the scope of the present disclosure.For example, the transcription unit 3814 may not include the ASR systems3820 c and 3820 d. In these and other embodiments, the first fuser 3824a may be omitted. The scorer 3816 may be configured, in this example, tocompensate for errors committed by the ASR system 3820 b in determiningCA performance.

FIG. 39 illustrates an example environment 3900 for transcriptiongeneration, in accordance with some embodiments of the presentdisclosure. In some embodiments, the environment 3900 may include fourtranscription units 3914 a-d, collectively the transcription units 3914.The transcription units 3914 b-d may be configured in a manner toprovide higher accuracy transcriptions than the transcription unit 3914a. In these and other embodiments, the components of the transcriptionunit 3914 b are illustrated. Transcription units 3914 c and 3914 d maybe configured in a similar manner or a different manner that provideshigher accuracy than the transcription unit 3914 a. In general, theoutput of a higher-accuracy transcription unit, such as thetranscription units 3914 b-d, may be used, as will be described below,for accuracy estimates, providing transcriptions to a user's device, andfor training ASR models.

In some embodiments, the transcription generated by the transcriptionunit 3914 b may be compared to the transcription generated by thetranscription unit 3914 a by the scorer 3916 to estimate accuracy of thetranscription unit 3914 a. Additionally or alternatively, thetranscription generated by the transcription unit 3914 b may be used toprovide transcriptions to user devices in certain situations including:

-   -   1. High-priority communication sessions or for subscribers        receiving premium service;    -   2. Difficult communication sessions, as determined by an        estimated error rate, detected accent, speaker demographics        (e.g., elderly, child, legal minor, speech or hearing        impairments), assessment of the speaker's voice clarity,        automatic detection of the spoken language, estimated topic        difficulty such as a conversation on a specialized topic,        measurement of signal quality such as noise level or distortion,        or other factors automatically detected; and    -   3. Communication sessions where extra CAs are available to        provide revoiced audio.

In some embodiments, the transcriptions of the transcription units 3914b-d may be provided to modeling tools 3904. The modeling tools 3904 maybe configured to train ASR models. In some embodiments, ASR models maybe built or adapted in real-time (i.e., “on-the-fly”), meaning that ASRsystem models are trained on non-stored production data (e.g.,communication session audio and/or generated transcriptions).Additionally or alternatively, ASR models may be built from pre-recordeddata such as recorded transcriptions from transcription units 3914 b-d.

As illustrated, the transcription unit 3914 b may include a first audiointerface 3918 a, a first speaker-dependent ASR system 3920 a, a secondspeaker-independent ASR system 3920 b, a fuser 3024, a second audiointerface 3918 b, and a text editor 3926, which may receive input from asecond CA different from the CA that provides the revoiced audio to thefirst audio interface 3918 a. Alternatively or additionally, thetranscription unit 3914 b may include a third speaker-dependent ASRsystem 3920 c that may include models based on the second CA. The firstaudio interface 3918 a may obtain the audio, broadcast the audio to thefirst CA, and obtain revoiced audio. The first audio interface 3918 amay provide the revoiced audio the first speaker-dependent ASR system3920 a that may generate a revoiced transcription of the revoiced audioand provide the revoiced transcription to the fuser 3924. The secondspeaker-independent ASR system 3920 b may also provide a transcriptionto the fuser 3924 based on the audio. The transcription and the revoicedtranscription may be fused by the fuser 3924 to create a first fusedtranscription.

In some embodiments, the second audio interface 3918 b may be configuredto provide the audio to the second CA. The text editor 3926 may beconfigured to present the fused transcription to the second CA andobtain edits to the fused transcription from the second CA. The secondCA may use a keyboard, mouse, and other computer interface devices,including the third speaker-dependent ASR system 3920 c configured tounderstand voice commands and/or transcribe revoiced audio. Fusedtranscriptions with corrections from the text editor 3926 may be denotedas higher-accuracy transcriptions.

In some embodiments, the audio provided by the second audio interface3918 b to the second CA may be delayed by a delay 3906 so that portionsof the fused transcription are visible via the text editor 3926approximately at the time the second audio interface 3918 b broadcaststhe corresponding audio.

In some embodiments, the delay 3906 may be configured to speed up orslow down speech that is sent to the second audio interface 3918 b,depending on where edits are being performed on the fused transcriptionin the text editor 3926. For example, when a word or phrase is selectedfor editing and/or when editing starts generally, the delay 3906 may beadjusted such that the second audio interface 3918 b plays audiocorresponding to the word or phrase. In some embodiments, the secondaudio interface 3918 b may rewind audio. In these and other embodiments,audio may subsequently be sped up by the delay 3906 to compensate forthe lost time. In some embodiments, the second audio interface 3918 bmay rewind audio based on inputs from the second CA using a voicecommand, click or key press, knob, or foot pedal, among other inputs.

In some embodiments, the delay 3906 may be configured to change a speechrate in the audio by changing the duration of silence segments betweenspeech segments. In some embodiments, silence segments may be locatedusing energy-based voice activity detection. Additionally oralternatively, silence segments may be located using an ASR system thatidentifies and reports word endpoints (the time of onset and offset) asthe ASR system reports the words recognized.

The transcription unit 3914 a may be configured to provide a hypothesistranscription to the scorer 3916. In some embodiments, the scorer 3916may compare the hypothesis transcription to the output of thetranscription unit 3914 b to generate an accuracy estimate. The estimatemay be used, for example to give the CA feedback or to benchmark averageperformance of the transcription unit 3914 a.

In some embodiments, the transcription units 3914 b-d may be used toprovide transcriptions to modeling tools 3904. The modeling tools 3904may generate language models, acoustic models, pronunciation models, andother types of ASR and machine learning models used in captioning.

The transcription unit 3914 b may also be used to provide transcriptionsto user devices for presentation to subscribers. For example, suppose anaccuracy estimator determines that a revoicing transcription unitassociated with a first CA is struggling to transcribe speech that is,for example, fast, difficult, or accented. One solution is to transferthe communication session to a different transcription unit that isassociated with a second CA with more appropriate skills. Another optionis to use the transcription unit 3914 b configuration. The transcriptionunit 3914 b configuration may use the first CA and a second CA tocorrect the text. Another option is to transfer the communicationsession to a transcription unit configured as the transcription unit3914 b that is associated with new CAs that interface with thetranscription unit as illustrated.

Modifications, additions, or omissions may be made to the environment3900 without departing from the scope of the present disclosure. Forexample, the environment 3900 may include more transcription units orfewer transcription units than illustrated.

FIG. 40 illustrates an example environment 4000 that includes a multipleinput ASR system 4002, in accordance with some embodiments of thepresent disclosure. The multiple input ASR system 4002 may be configuredto process multiple audio inputs. The multiple audio inputs may includereceived audio and revoiced audio. The received audio may benon-revoiced audio. As illustrated, the multiple audio inputs includeaudio and revoiced audio from each of three audio interfaces 4018 a-c,collectively audio interfaces 4108. The multiple input ASR system 4002may combine information from the multiple audio streams to create atranscription. The transcription may be used to provide transcriptionsto a user device and for use with other methods such as those in Tables12 and 14.

In some embodiments, the multiple input ASR system 4002 may compareacoustic evidence from the revoiced audio with the received audio and ingenerating a transcription, may consider factors such as estimatednon-revoiced ASR performance with respect to the audio, estimatedrevoiced ASR performance with respect to the audio, and indicators thatthe revoiced audio is silent when the audio includes words and/or a CAgenerating the revoiced audio may be distracted as the CA is makingcorrections to the transcription.

Although FIG. 40 is illustrated with three audio interfaces 4018 thateach interface with a different CA, it is contemplated that there may bemore or less than three audio interfaces 4018. Alternatively oradditionally, the audio from the audio interfaces 4018 and not theregular audio may be input to the multiple input ASR system 4002. Insome embodiments, inputs to the multiple input ASR system 4002 mayinclude multiple versions of revoiced and regular audio, where eachversion may differ from other versions in terms of audio quality, delay,or in other respects. For example, the regular audio may be sampled at 8kHz with 8-bits of resolution and compressed (e.g., using mu-Lawencoding) and the revoiced audio may be sampled at 16 kHz with 16 bitsof resolution and no compression. As a result, in some embodiments,sampling rates, resolution, and compression for the audio obtained bythe multiple input ASR system 4002 and provided to a joint processor4010 may be different.

In some embodiments, the multiple input ASR system 4002 may beconfigured to receive input from the audio interfaces 4018 and from theregular audio. The multiple input ASR system 4002 may include featureextractors 4004 a and 4004 b, collectively the feature extractors 4004,for extracting features from the revoiced audio and regular audio,respectively.

The outputs of the feature extractors 4004 may be communicated to thejoint processor 4010. The joint processor 4010 may include components ofan ASR system as described above with reference to FIG. 5, including toa feature transformer, probability calculator, rescorer, capitalizer,punctuator, and scorer, among others.

In some embodiments, the multiple input ASR system 4002 may be providedwith an audio delay 4006. The audio delay may be configured tocompensate for the revoiced audio and the regular audio arriving at themultiple input ASR system 4002 at different times. The audio delay 4006may add a delay into one or both of the processing paths of the revoicedand regular audio to better synchronize the revoiced and regular audio.In some embodiments, the audio delay 4006 may be variable, responding tothe relative latency between the two paths of the revoiced and regularaudio. Alternatively or additionally, the audio delay 4006 may be fixed,such as based on the average relative latency.

Modifications, additions, or omissions may be made to the environment4000 without departing from the scope of the present disclosure. Forexample, the audio delay 4006 is illustrated placed in the regular audiopath before the feature extractors 4004, however, the audio delay 4006may alternatively be placed after the feature extractors 4004.Alternatively or additionally, each audio input may include an audiodelay in the audio or feature extraction path/output. In anotherexample, the feature extractors 4004 are illustrated as separate fromeach other, one per audio input, but they may be combined into a singlefeature extractor with multiple audio inputs.

FIG. 41 illustrates an example environment 4100 for determining an audiodelay, in accordance with some embodiments of the present disclosure.The environment 4100 may include an ASR system 4120 a and 4120 b,collectively the ASR systems 4120. In some embodiments, audio isprovided to an ASR system 4120 b and an audio interface 4118. The audiointerface 4118 may generate revoiced audio based on the audio inconnection with a CA. The revoiced audio may be provided to an ASRsystem 4120 a. The ASR systems 4120 may generate transcriptions based onthe received audio and revoiced audio. The ASR system 4120 may markendpoints for the beginning or ending of words in the transcriptions.The endpoints and the transcription may be provided to an audio delay4104. The audio delay 4104 may align the two transcriptions and may usethe relative positions of endpoints between the two transcriptions todetermine a delay value between the revoiced audio and the regularaudio. For example, if the ends of words transcribed by the ASR system4120 a are, on average, two seconds later than the corresponding ends ofwords transcribed by ASR system 4120 b, then the delay may be set to twoseconds.

The delay between the received audio and the revoiced audio may becompensated for by delaying the audio. Alternatively or additionally,the revoiced audio and the regular audio may be provided to another ASRsystem(s). In these and other embodiments, after features are extractedfrom the revoiced audio and the regular audio, the features may bedelayed to align the transcriptions. Alternatively or additionally, thedelay may be compensated for in other portions of the flow of an ASRsystem. In these and other embodiments, the ASR systems 4120 may beconfigured to operate in a reduced mode or less effectively than asubsequent ASR system as the ASR systems 4120 may be used to determinethe delay between the regular audio and the revoiced audio. In these andother embodiments, the ASR system 4120 may provide other information,such as grammars, accuracy information, or other information to anotherASR system that may generate a transcription that may be sent to a userdevice.

In some embodiments, the transcription generated by the ASR system 4120b may be used to provide a grammar input including, for example, asingle phrase, to the ASR system 4120 a so that the ASR system 4120 acreates substantially the same transcription as the ASR system 4120 bbut with different endpoints. Additionally or alternatively, the grammarinput to the ASR system 4120 a from the ASR system 4120 b may include ann-best list, WCN, lattice, word graph, or other format that allows theASR system 4120 a to select from among multiple options. In someembodiments and for reduced latency, the transcription output of the ASRsystem 4120 b may be used to provide a grammar input to the ASR system4120 a.

Additionally or alternatively, the output of the ASR systems 4120 may beused to generate features for selectors, estimators, and classifiers.The text output of ASR systems 4120 may be compared to each other and/orto other ASR systems to determine agreement rates, which may serve asfeatures, as described with reference to FIG. 21 and items #14-16 ofTable 5. One or more confidence outputs of ASR systems 4120 may also beused as features. For example, a first ASR system may transcribe a firstaudio signal to create a first hypothesis. A second ASR system maytranscribe a second audio signal using a grammar derived from the firsthypothesis. The second ASR system may generate a phrase confidence scoreand/or confidence scores for individual words. The confidence scores maybe used as features (see item #102, Table 5).

Modifications, additions, or omissions may be made to the environment4100 without departing from the scope of the present disclosure. Forexample, the regular audio and the revoiced audio may be provided to amultiple input ASR system, such as the multiple input ASR system 4002 ofFIG. 40.

FIG. 42 illustrates an example environment 4200 where a first ASR system4220 a guides the results of a second ASR system 4220 b, in accordancewith some embodiments of the present disclosure. In some embodiments,the first ASR system 4220 a may transcribe audio to generate a firsttranscription. The first ASR system 4220 a may also generate an outputrepresenting multiple hypotheses such as an n-best list, WCN, lattice,or word graph. The output may be converted by a Language Model (LM)converter 4202 to a grammar or second language model LM2.

The audio interface 4218 may provide revoiced audio based on the audioto the second ASR system 4220 b. The second ASR system 4220 b may usethe second language model LM2 to transcribe the revoiced audio togenerate a second transcription. The second ASR system 4220 b mayfurther use a third generic language model LM3 to create the secondtranscription.

In some embodiments, the second LM2 and third LM3 language models may beused by the second ASR system 4220 b, for example, to: (a) interpolateboth the second LM2 and third LM3 language models into an interpolatedlanguage model, (b) interpolate the second LM2 and third LM3 languagemodels at runtime, or (c) to combine the second LM2 and third LM3language models in a hierarchal language model configuration.Additionally or alternatively, the second transcription may be edited bya text editor 4226 and then sent to a user device.

In some embodiments, the first and second transcriptions may be alignedand fused by a fuser 4224 to create a fused transcription, edited by thetext editor 4226, and sent to a user device. The first transcription maybe delayed by an audio delay 4204 to account for latency incurred by theaudio interface 4218 and the second ASR system 4220 b so that the firstand second transcriptions arrive at the fuser 4224 more closely aligned.

In some embodiments, the first ASR system 4220 a may add new elements tothe multiple hypotheses output over time as the first ASR system 4220 areceives and decodes new audio. For example, as the first ASR system4220 a decodes new audio, the first ASR system 4220 a may add new arcsto the lattice or word graph representation of the multiple hypotheses.In some embodiments, the first ASR system 4220 a may add new elements tothe multiple hypotheses in time periods that overlap with the second ASRsystem 4220 b decoding the revoiced audio using the previous hypotheses.In some embodiments, new elements that the first ASR system 4220 a addsto the multiple hypotheses may be added or appended to the secondlanguage model in real time. In these and other embodiments, the secondASR system 4220 b may consider the new elements as possibilities inconstructing the second transcription. Sufficient delay may be insertedin the path from audio through generating the transcription by thesecond ASR system 4220 b to give the second ASR system 4220 b time toreceive and incorporate the updated second language model by the timethe corresponding revoiced audio arrives.

An example implementation of the environment 4200 is now provided. Thefirst ASR system 4220 a may use a first language model to transcribecommunication session audio into a first transcription and a multiplehypotheses output, such as in the form of a lattice. The LM converter4202 may convert the multiple hypotheses output to a second languagemodel. In some embodiments, if there is a preexisting second languagemodel, elements of the multiple hypotheses may be combined with thesecond language model to modify the second language model. Additionallyor alternatively, if there is a pre-existing second language model, itmay be replaced with an updated language model.

In these and other embodiments, an audio interface 4218 may providerevoiced audio based on the audio to the second ASR system 4220 b. Thesecond ASR system 4220 b may use the second language model to transcribethe revoiced audio to generate the second transcription. In someembodiments, the second ASR system 4220 b may further use a thirdlanguage model to generate the second transcription. The secondtranscription may be sent to a user device for display. In a variationon this step, the first and second transcriptions may be fused, thensent to the user device for display. Modifications, additions, oromissions may be made to the environment 4200 without departing from thescope of the present disclosure.

FIG. 43 is a flowchart of another example method 4300 of fusingtranscriptions in accordance with embodiments of the present disclosure.The method 4300 may be arranged in accordance with at least oneembodiment described in the present disclosure. The method 4300 may beperformed, in some embodiments, by processing logic that may includehardware (circuitry, dedicated logic, etc.), software (such as is run ona general-purpose computer system or a dedicated machine), or acombination of both. In some embodiments, the method may be performed bythe fuser 124 of FIG. 1 among other fusers described in this disclosure.In these and other embodiments, the method 4300 may be performed basedon the execution of instructions stored on one or more non-transitorycomputer-readable media. Although illustrated as discrete blocks,various blocks may be divided into additional blocks, combined intofewer blocks, or eliminated, depending on the desired implementation.

The method 4300 may begin at block 4302, where first audio dataoriginating at a first device during a communication session between thefirst device and a second device may be obtained. In some embodiments,the communication session may be configured for verbal communicationsuch that the first audio data includes speech.

At block 4304, a first text string that is a transcription of the firstaudio data may be obtained. In some embodiments, the first text stringmay be generated by a first automatic speech recognition engine usingthe first audio data and using a first model trained for multipleindividuals. In these and other embodiments, the first model may includeone or more of the following: a feature model, a transform model, anacoustic model, a language model, and a pronunciation model.

At block 4306, a second text string that is a transcription of secondaudio data may be obtained. In some embodiments, the second audio datamay include a revoicing of the first audio data by a captioningassistant. In these and other embodiments, the second text string may begenerated by a second automatic speech recognition engine using thesecond audio data and using a second model trained for the captioningassistant.

At block 4308, an output text string from the first text string and thesecond text string may be generated. In some embodiments, the outputtext string may include one or more first words from the first textstring and one or more second words from the second text string. In someembodiments, generating the output text string may further includedenormalizing the first text string and the second text string, aligningthe first text string and the second text string, and comparing thealigned and denormalized first and second text strings.

In some embodiments, generating the output text string may furtherinclude selecting the one or more second words based on the first textstring and the second text string both including the one or more secondwords and selecting the one or more first words from the first textstring based on the second text string not including the one or morefirst words.

At block 4310 the output text string may be provided as a transcriptionof the speech to the second device for presentation during thecommunication session concurrently with the presentation of the firstaudio data by the second device.

Modifications, additions, or omissions may be made to the method 4300without departing from the scope of the present disclosure. For example,the operations of method 4300 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areonly provided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

For example, in some embodiments, the method 4300 may include correctingat least one word in one or more of: the output text string, the firsttext string, and the second text string based on input obtained from adevice associated with the captioning assistant. In these and otherembodiments, the input obtained from the device may be based on a thirdtext string generated by the first automatic speech recognition engineusing the first audio data. In some embodiments, the first text stringand the third text string may both be hypothesis generated by the firstautomatic speech recognition engine for the same portion of the firstaudio data.

In some embodiments, the method 4300 may further include obtaining athird text string that is a transcription of the first audio data or thesecond audio data. In these and other embodiments, the third text stringmay be generated by a third automatic speech recognition engine using athird model. In these and other embodiments, the output text string maybe generated from the first text string, the second text string, and thethird text string.

In some embodiments, the third text string may be a transcription of thefirst audio data. In these and other embodiments, the method 4300 mayfurther include obtaining a fourth text string that is a transcriptionof the second audio data. In these and other embodiments, the fourthtext string may be generated by a fourth automatic speech recognitionengine using the second audio data and using a fourth model. In theseand other embodiments, the output text string may be generated from thefirst text string, the second text string, the third text string, andthe fourth text string.

In some embodiments, the method 4300 may further include obtaining thirdaudio data that includes speech and that originates at the first deviceduring the communication session and obtaining a third text string thatis a transcription of the third audio data. In these and otherembodiments, the third text string may be generated by the firstautomatic speech recognition engine using the third audio data and usingthe first model. The method 4300 may further include in response toeither no revoicing of the third audio data or a fourth transcriptiongenerated using the second automatic speech recognition engine having aquality measure below a quality threshold, generating an output textstring using only the third text string.

In some embodiments, the accuracy of transcriptions generated bytranscription units may be measured. For example, the accuracy oftranscriptions generated by a single revoiced transcription unit, anon-revoiced transcription unit, or a group of transcription units maybe measured. Alternatively or additionally, the accuracy may be measuredfor benchmarking accuracy of one or more transcription units. In theseand other embodiments, the accuracy of transcriptions may be measured inreal-time production of the transcriptions without relying on recording,saving or offline transcription of audio. FIGS. 44-59 illustrate variousembodiments that discuss systems and methods that may be used to measurethe accuracy of transcriptions.

FIGS. 44-55, among others, describe various systems and methods that maybe used to determine statistics with respect to transcriptions of audiogenerated by ASR systems. In some embodiments, the statistics mayinclude errors, including error types; accuracy, error rate; confidencescores; among other types of statistics. In some embodiments, thestatistics may be generated by comparing a reference transcription to ahypothesis transcription. In these and other embodiments, the referencetranscriptions may be generated based on the generation of higheraccuracy transcriptions as described in FIGS. 31-43. Alternatively oradditionally, the statistics of the transcriptions may be generated inreal-time without long-term recording of the audio.

FIG. 44 illustrates an example environment 4400 for scoring atranscription unit, in accordance with some embodiments of the presentdisclosure. In some embodiments, the environment 4400 may be configuredto measure the accuracy of transcriptions of audio of a communicationsession generated by a transcription unit 4414 without capturing theaudio of the communication session. In some embodiments, thetranscription unit 4414 may be a revoiced transcription unit thatobtains a revoicing of audio through a CA and generates a transcriptionbased on the revoiced audio. Alternatively or additionally, theenvironment 4400 may also be used to measure accuracy of transcriptionfor other transcription unit configurations.

In some embodiments, the output of the environment 4400 may include atotal number of errors for a transcription, percentage of words that areerrors, a count of each error type, a total number of words in areference transcription, a total number of words in a non-referencetranscription, a total number of words in a reference and non-referencetranscription, an error rate, an accuracy percentage, a performancemetric including one or more measurements such as ASR system accuracy,estimated transcription difficulty of the audio sample, or anotherperformance metric such as capitalization accuracy and/or punctuationaccuracy. The accuracy estimate, error output, or other performancemetrics may be used to provide feedback to a CA, generate reports,and/or to benchmark average ASR system performance. Additionally oralternatively, the output of the environment may also be used, alone orin combination with one or more selectors, estimators, and classifiers,to generate a decision regarding selecting between transcription units,such as selecting between a revoiced or non-revoiced transcription unit.In these and other embodiments, a revoiced transcription unit mayinclude one or more ASR systems that may use revoiced audio to generatea transcription. In these and other embodiments, the revoicedtranscription unit may also include one or more ASR systems that may useregular audio to generate a transcription. In contrast, a non-revoicedtranscription unit may not include any ASR systems that use revoicedaudio to generate a transcription.

An ASR system 4420 may generate a transcription based on audio andprovide the transcription to a scorer 4402. The transcription unit 4414may generate a transcription based on revoicing of the audio and providethe transcription to scorer 4402. The scorer 4402 may also obtain theaudio.

In some embodiments, the scorer 4402 may be configured to determine theaccuracy of the transcriptions. The scorer 4402 may be configured topresent the transcriptions to a first judge 4404 a and a second judge4404 b, collectively, the judges 4404. The judges 4404 may be humans.The scorer 4402 may provide a graphical user interface configured toreceive input from one or more of the judges 4404. Based on the inputfrom the judges 4404, the scorer 4402 may determine a number of errorsdetected for each transcription.

In some embodiments, the judges 4404 may listen to the audio and reviewthe transcriptions to identify errors. The judges may provide theidentified errors to the scorer 4402. In these and other embodiments,each of the judges 4404 may review and score at least a portion of thevarious transcriptions.

In some embodiments, the scorer 4402 may be configured to provide aninterface for the judges 4404 to transcribe at least part of the audiosample to create a reference transcription. In these and otherembodiments, the scorer 4402 may be configured to allow the judge 4404to rewind, skip, skip silence portions, jump to determined points in theaudio, such as a point corresponding to a selected point in a drafttranscription or a point in a displayed waveform indicated by a judge,slow down, speed up, fast forward, or replay portions of the audio inthe transcription process.

In some embodiments, in response to a reference transcription beingcreated, an automated scoring process, such as one or more of thesystems described below with reference to FIGS. 55 and 56, may be usedto compare the transcriptions from the transcription unit 4414 and/orthe ASR system 4420 with the reference transcription to determineaccuracy of the transcriptions from the transcription unit 4414 and/orthe ASR system 4420. In some embodiments, the automated scoring processmay create an error map to be used by the judges 4404. The scorer 4402may enable the judges 4404 to examine and correct errors in theautomated scoring process.

In some embodiments, the judges 4404 may listen to at least part of theaudio and provide input with respect to errors in at least part of thetranscriptions from the transcription unit 4414 and/or the ASR system4420. For example, the judges 4404 may score substantially all of thetranscriptions from the transcription unit 4414 and/or the ASR system4420. Alternatively or additionally, the judges 4404 may score part ofthe transcriptions and leave another part of the transcriptionsunscored.

In some embodiments, the scorer 4402 may provide an indication of theerrors to an error counter 4406. The error counter 4406 may use theerrors to determine an output of the environment 4400, such as one ofthe performance metrics discussed above.

In some embodiments, a selected time after the end of a communicationsession that is providing the audio, the audio and transcriptions of theaudio may be deleted and scoring may be discontinued. In these and otherembodiments, accuracy results of the transcription may be stored. Insome embodiments, accuracy results may include the results of scoringone or more portions of the audio of the communication session and mayexclude other portions of the audio.

In some embodiments, the scorer 4402 may not determine an accuracy ofthe transcription from the ASR system 4420. In these and otherembodiments, the scorer 4402 may be configured to align thetranscriptions from the ASR system 4420 and the transcription unit 4414.The scorer 4402 may use the transcription from the ASR system 4420 as areference transcription that is compared to the transcription from thetranscription unit 4414. The differences may be considered potentialerrors of the transcription from the transcription unit 4414 and may beflagged and presented to the judges 4404.

In these and other embodiments, the judges 4404 may mark or confirmerrors by clicking on flagged errors. The judges 4404 may also selectother errors, such as incorrect, inserted, or deleted words in thetranscription. In these and other embodiments, the judges 4404 mayrewind audio as needed to review the audio to confirm errors.Alternatively, judges 4404 may use voice commands, keyboards, or otherforms of computer input to interact with the audio and/ortranscriptions.

In some embodiments, the error counter 4406 may be configured to counterrors marked or confirmed by the judges 4404. In these and otherembodiments, the error counter 4406 may count all errors together or itmay count errors separately. For example, the error counter 4406 mayseparately count deletion, insertion, and substitution errors. In someembodiments, following the termination of a communication sessionproviding the audio or after a selected amount of time (a few seconds toa few minutes) after termination of the communication session, thescorer 4402 may delete all audio and/or text to protect the privacy ofthe participants in the communication session.

In some embodiments, the scorer 4402 and the judges 4404 may access a CAinterface through a CA client of the transcription unit 4414 to obtaininformation for scoring. For example, a scoring GUI may use a remotedesktop to connect to a CA client and allow a judge to listen to audioand/or the revoiced audio, view the screen being viewed by a CA, readthe transcriptions generated by the speaker-dependent ASR system of thetranscription unit 4414, and view edits provided by the CA. In these andother embodiments, the scoring GUI may also provide an interface for thejudges 4404 to score the transcription.

Modifications, additions, or omissions may be made to the environment4400 without departing from the scope of the present disclosure. Forexample, the environment 4400 may include denormalizers that may beconfigured to denormalize the transcriptions before the transcriptionsare provided to the scorer 4402. Alternatively or additionally, theenvironment 4400 may not include the ASR system 4420. Alternatively oradditionally, the environment 4400 may include one judge or more thantwo judges.

FIG. 45 illustrates another example environment 4500 for scoring atranscription unit, in accordance with some embodiments of the presentdisclosure. In some embodiments, the depicted embodiment illustratesanother embodiment for monitoring and measuring accuracy of atranscription.

In some embodiments, an audio interface 4518 may obtain audio and arevoicing of the audio from a CA. The audio interface 4518 may providethe revoiced audio to a speaker-dependent ASR system 4520 that maygenerate a transcription of the revoiced audio. In these and otherembodiments, a text editor 4526 may obtain input from the CA and applyedits to the transcription. The edited transcription may be denormalizedby a denormalizer 4503 and provided to a comparer 4504. Thetranscription may be referred to as a monitored transcription.

In some embodiments, an accuracy monitor 4502 may be provided and mayinclude the denormalizer 4503, the comparer 4504, a counter 4506, afuser 4524, an accuracy estimator 4508, a set 4510 of ASR systems, and adenormalizer 4512. Audio may also be received at the accuracy monitor4502. Each of the ASR systems of the set 4510 may generate atranscription. Each of the transcriptions may be provided to the fuser4524 for combination of the transcriptions to generate a fusedtranscription. The fused transcription may be denormalized by thedenormalizer 4512 and the denormalized fused transcription, referred toas the reference transcription, may be provided to the comparer 4504.

In some embodiments, the comparer 4504 may be configured to compare themonitored transcription with the reference transcription. In these andother embodiments, the comparer 4504 may compare the monitoredtranscription with the reference transcription by determining an editdistance or Levenshtein distance there between. In some embodiments, thecomparison process by the comparer 4504 may be implemented as follows:(1) the comparer 4504 may align the monitored transcription and thereference transcription; (2) the comparer 4504 may compare each alignedpair of tokens from the monitored transcription and the referencetranscription. The pair of tokens may include a first token from themonitored transcription and a second token from the referencetranscription; (3) the comparer 4504 may provide an indication, such asa match or no match with respect to each aligned pair of tokens, to thecounter 4506. For example, the comparer 4504 may output a zero when apair of tokens match and a one if there is no match between a pair oftokens; and (4) the number of differences are counted or averaged by thecounter 4506 to determine an average disagreement rate, edit distance,and/or Levenshtein distance.

In some embodiments, the disagreement rate as determined by the counter4506 may be used to estimate accuracy of the ASR system 4520 or, asillustrated, it may be combined with other features (see Table 2 andTable 5) by an accuracy estimator 4508 to estimate accuracy of the ASRsystem 4520. In these and other embodiments, the accuracy monitor 4502may be configured to apply the same features to measuring agreementrates when the monitored transcription is generated using revoiced ornon-revoiced audio. In some embodiments, a report may be generated thatincludes the output of the accuracy estimator 4508. For example, thereport may be generated after each communication session, daily, weekly,etc.

The report, including the estimated accuracy of the revoiced ASR system4520 generated by the accuracy monitor 4502, may be used for one or moreof multiple purposes, including:

-   -   1. Advise a CA interfacing with the audio interface 4518 and the        text editor 4526 on specific errors.    -   2. Alert the CA in real-time of a potential error so that the CA        may correct the error. In these and other embodiments, the        accuracy monitor 4502 may estimate a confidence value of        certainty that the CA has made an error. If a confidence value        exceeds a first threshold, the accuracy monitor 4502 may cause        the text editor 4526 to highlight the potential error and may        propose alternative words or phrases for the CA to select. If        the confidence value exceeds a second threshold, the accuracy        monitor 4502 may automatically correct the potential error,        cause the text editor 4526 to display the correction, and        provide a method for the CA to override the correction.    -   3. Advise the CA on quality measures such as accuracy or error        rates for one or more communication sessions. The quality        measure may be absolute (e.g., 89%), relative to the past        performance of the transcriptions generated by the revoiced ASR        system 4520 (e.g., “3% better than yesterday” or “2% below the        best”), relative to other transcriptions generated by the        revoiced ASR systems (e.g., “5% above the team average” or “3%        below last week's top revoiced ASR system”), or it may include        other statistics, such as statistics derived from the        performance of the systems.    -   4. Present a visual and or audio instruction or assessment to        the CA regarding performance. This instruction may be a        motivational message such as “Good job!” “You can do better” or        “The transcription scored 93% on that last communication        session. That's your best today.” The text, including        non-numeric text, of the message may be responsive to the CA        history and current performance.    -   5. Display a dial, thermometer, chart, or other graphics        illustrating performance.

Modifications, additions, or omissions may be made to the environment4500 without departing from the scope of the present disclosure. Forexample, the environment 4500 may not include the denormalizer 4512 whenthe fuser 4524 includes denormalizing capability. Alternatively oradditionally, the set 4510 of ASR systems may be a single ASR system. Inthese and other embodiments, the set 4510 of ASR systems may not includethe fuser 4524.

In some embodiments, the accuracy monitor 4502 may be configured with anadder on the output of the counter 4506 or that is part of the counter4506. In these and other embodiments, the accuracy estimator 4508 may beconfigured to determine a correction factor to be added by the adder tothe disagreement rate provided by the counter 4506. The correctionfactor may be used, for example, to refine the ASR accuracy estimate orto compensate for ASR and other errors in the accuracy monitor 4502.

As with other estimators described herein, the accuracy estimator 4508may use input features such as a quality, accuracy, or a confidencemeasure reported by the ASR systems, historical revoicing andnon-revoicing transcription accuracy, agreement rates between ASRsystems, and other features described in Table 2 and Table 5. Theaccuracy estimator 4508 may also use methods such as DNNs, weightedsums, and other methods from Table 9. Additionally or alternatively, theaccuracy estimator 4508 may also be very simple and just apply aconstant correction factor to the disagreement rate.

FIG. 46 illustrates an example environment 4600 for generating anestimated accuracy of a transcription, in accordance with someembodiments of the present disclosure. The environment includes a groupof transcription units 4616, including a first transcription unit 4616a, a second transcription unit 4616 b, and a third transcription unit4616 c. The transcription units 4616 may be revoiced, non-revoiced, or acombination of revoiced and non-revoiced transcription units.Alternatively or additionally, each of the transcription units 4616 maybe configured in a unique or a similar manner with respect to anyconfigurations described in this disclosure. The transcription units4616 may obtain audio and generate transcriptions that are provided to afuser 4624. The fuser 4624 may combine the transcriptions to generate afused transcription. The fused transcription may be a higher accuracytranscription than the output of one of the transcription units 4616.

The higher-accuracy transcription may be used in multiple ways includingthose enumerated in Table 12 and 13 and below in Table 14.

TABLE 14 1. The higher-accuracy transcription may be stored, whenlegally allowed, together with audio and other data associated with thecommunication session. Stored data may then be used for purposes such asevaluation and training of CAs, quality assurance, accuracybenchmarking, and ASR modeling. 2. The higher-accuracy transcription maybe used to train speech recognition models, including language models,acoustic models, capitalization models, punctuation models, and speakeradapted models. This arrangement and other fusion implementationsdescribed herein may be used to generate transcriptions for trainingmodels on- the-fly in cases where recording of production communicationsessions is prohibited. 3. The higher-accuracy transcription may be sentas a transcription to a user device. The higher-accuracy transcriptionmay be used for communication sessions that are otherwise challengingbecause of noise, accents, speech from a child, etc., or when thecommunication session has a higher-priority. 4. Transcriptions fromother transcription units may be compared to the higher-accuracytranscription using one or more scorers. The resulting score may be usedto evaluate the transcription units.

A transcription unit 4614 may also be configured obtain the audio and togenerate a transcription. The transcription unit 4614 may be a revoicedor non-revoiced transcription unit. Alternatively or additionally, thetranscription unit 4614 may be configured in any manner described inthis disclosure.

In some embodiments, the fused transcription from the fuser 4624 and thetranscription from the transcription unit 4614 may be provided to ascorer 4604. The scorer 4604 may align and determine an estimatedaccuracy of the transcription from the transcription unit 4614. In someembodiments, the scorer determines an estimated accuracy based on anagreement rate between the two input transcriptions. The scorer 4604 mayoutput the estimated accuracy to a multivariate estimator 4602. Themultivariate estimator 4602 may include a neural network, linearestimator, or another form of estimator configured to use multipleinputs. The multivariate estimator 4602 may be configured to useestimation features 4608, such as those in Table 2 and Table 5, torefine the estimation accuracy from the scorer 4604. For example, themultivariate estimator 4602 may adjust the estimation accuracy based onestimation features associated with the transcription unit 4614, thetranscription units 4616, and fuser 4624. For example, based on anaccuracy of the fused transcription in the estimation features, theestimation accuracy of the transcription may be adjusted.

In some embodiments, the multivariate estimator 4602 may use anestimation model. The estimation model may be trained using, forexample, one or more of the methods in Table 9. In some embodiments, anestimation model may be trained. For example, audio samples and valuesfor features from Table 2 and Table 5 associated with the audio samplesmay be obtained. An error rate of a transcription unit, such as thetranscription unit 4614, for each audio sample may be obtained. For eachaudio sample, values for features from Table 2 and Table 5 may beprovided to a machine learning algorithm with the error rate associatedwith the sample, such that a model may be generated that is designed toestimate the error rate from the features. In these and otherembodiments, the features of the transcription unit 4614 may be providedto the multivariate estimator 4602. The multivariate estimator 4602 mayuse the model to determine an estimated error rate based on an errorrate of one or more transcription units, such as transcription units4614 a-c, features from fuser 4624, features of the transcription unit4614, the estimated accuracy from the scorer 4604, and one or more otherfeatures such as features from Table 2 and Table 5.

Modifications, additions, or omissions may be made to the environment4600 without departing from the scope of the present disclosure. Forexample, although three transcription units are illustrated in thetranscription units 4616, in some embodiments more or less than threetranscription units may be used in the group of transcription units4616. In some embodiments, one transcription unit may be used. In theseand other embodiments, the fuser 4624 may not be used. Alternatively oradditionally, the transcription provided to the scorer 4604 from thetranscription unit 4614 may be a fused transcription based ontranscriptions from multiple transcription units. In these and otherembodiments, the multiple transcription units may be the same,different, or some combination of the same and different transcriptionunits in any configuration of transcription units as discussed in thisdisclosure.

FIG. 47 illustrates another example environment 4700 for generating anestimated accuracy of a transcription, in accordance with someembodiments of the present disclosure. In some embodiments, theenvironment 4700 may be configured to measure an accuracy of atranscription of audio generated by a transcription unit 4714 withoutrecording the audio. The transcription unit 4714 may transcribe at leastpart of the audio to create a hypothesis transcription for which theaccuracy may be determined.

A reference transcription may be also be generated. A transcription unit4730 may obtain the audio and generate a reference transcription duringa document creation stage 4720. The transcription unit 4730 may be arevoiced or non-revoiced transcription unit or include any otherconfiguration of transcription units as described in this disclosure.The reference transcription may also be edited during two editing stages4722 a and 4722 b, collectively the editing stages 4722. Each editingstage 4722 may include a text editor 4742 that may be used by a CA tocorrect errors in the reference transcription. Although two editingstages 4722 are shown; however, there may be more or less, depending ona desired accuracy of the reference transcription. Each editing stage4722 in this configuration may be considered to be working in series, aseach editing stage 4722 may use the output of a previous editing stage4722. Thus, each editing stage 4722 may obtain the reference document asedited by a previous editing stage and may make further corrections.

In some embodiments, each of the editing stages 4722 may include anaudio interface 4744 and a text editor 4742. In these and otherembodiments, the audio may be provided by the audio interface 4744 to aCA. The CA may also view the reference transcription on the text editor4742 and provide input to the text editor 4742 to edit the referencetranscription.

In some embodiments, audio delays 4740 may be provided as part of eachstage of generating the reference transcription to delay the audioprovided to each subsequent stage. The audio may be delayed betweensubsequent stages of editing the transcription so that the audiopresented by the audio interface 4744 may be more closely synchronizedto the portion of text being displayed for and/or edited by a CA throughthe text editor 4742. The delay time may be constant or variable and maybe responsive to endpoints and text from a previous stage.

Methods for determining delay time are described above at least withreference to FIGS. 1, 33 a, 33 b, 33 c, and 48. In some embodiments, acontrol signal for each audio delay 4740 may include ASR endpointsand/or text. For example, the audio delay 1 4740 a may receive endpointsand text from the transcription unit 4730 and audio delay 2 4740 b andaudio delay 3 4740 c may receive text from text editor 1 4742 a and texteditor 2 4742 b, respectively. When an audio delay 4740 receives text,the audio delay 4740 may use an ASR system to generate endpoints, asillustrated in FIG. 48, to determine how much to delay the audio.

In some embodiments, the reference and hypothesis transcriptions may bedenormalized using denormalizers 4702 a and 4702 b, respectively. Thedenormalized reference and hypothesis transcriptions may be provided toa scorer 4704. The scorer 4704 may generate results by comparing thedenormalized reference and hypothesis transcriptions. The results of thescorer 4704 may include the error rate of the transcription unit 4714and details regarding how the score was calculated. For example, thedetails may include the aligned transcriptions in the form of an errormap. A viewer/editor 4708 may enable a quality assurance agent to listento the audio as provided by an audio interface 4744 c and verify thescore. The quality assurance agent may review, edit, approve, or discardthe results of the scorer 4704. A final score generator 4712 may formator analyze results from the viewer/editor 4708 to determine real timeaccuracy of the transcription unit 4714.

In some embodiments, the reference transcription in any one of thedocument creation stage 4720 and the editing stages 4722, may beprovided to a user device or in a method to determine corrections of atranscription provided to a user device where the corrections areprovided to the user device.

After audio has been scored, the audio may be deleted. In someembodiments, if the audio terminates before scoring is completed, theaudio may be deleted and further work on scoring may end. An accuracyfigure representing scored portions of the audio may be reported.Additionally or alternatively, audio may be preserved until scoring iscomplete or until transcriptions are delivered to a user device.

The accuracy and validity of an accuracy estimate based on theenvironment 4700 may be verified using a corpus of recorded audio withverified transcriptions. Audio from the corpus may be presented to theenvironment 4700 and scored as if the corpus were being received in realtime to generate real time accuracy of the hypothesis transcription ofthe audio. The reference transcriptions may also be compared to theverified transcriptions of the corpus to generate a first comparison.

In these and other embodiments, a second comparison may also begenerated. To generate the second comparison the recorded audio of thecorpus may be transcribed using the transcription unit 4714 to createcorpus hypothesis transcriptions. The corpus hypothesis transcriptionsmay then be compared with verified transcriptions to determine averified accuracy of the transcription unit 4714. The verified accuracymay then be compared to the real time accuracy determined using theenvironment 4700 to generate a second comparison. The first and secondcomparisons may be used to verify the integrity of the environment 4700.Thereafter, the specific configuration of the transcription unit 4714may be confidently used to score live communication session audio inreal-time.

In some embodiments, the final score generator 4712 may be configured tocorrect the accuracy estimates using estimators, such as the accuracyestimator and multivariate estimator in FIGS. 45 and 46 respectively.The estimators may be trained with recorded audio using the validationand calibration method described above with reference to the environment4700. An example of an estimator used for this purpose may include acorrection factor, determined by subtracting the verified accuracy fromthe real-time accuracy. When operating in real-time (e.g., not fromrecorded data), the correction factor may be added to the output of theviewer/editor to determine real-time accuracy.

Modifications, additions, or omissions may be made to the environment4700 without departing from the scope of the present disclosure. Forexample, in some embodiments, the environment 4700 may not include thetranscription unit 4730. In these and other embodiments, the CAassociated with the audio interface 4744 a may type the transcriptionfrom the audio, rather than starting from the reference transcriptionfrom the transcription unit 4730.

As another example, the environment 4700 may be configured to allow forparallel editing of a reference transcription, as opposed to the serialediting process. In these and other embodiments, the audio interfaces4744 and the text editors 4742 may present the audio and referencetranscription to multiple CAs in parallel, such as in overlapping timeperiods. In these and other embodiments, the text editors 4742 may allowfor multiple CAs to simultaneously edit or edit in overlapping timeperiods the reference transcription to correct errors in the referencetranscription.

In some embodiments, the audio interfaces 4744 may provide mechanismsfor CA to rewind, forward, speed up, or slow down audio. The segments ofaudio may be played to the CA automatically based on signals from thetext editors 4742. For example, the selection of a segment of audio tobe played to a CA may be responsive to the segment of text for which thetext editors 4742 may be receiving edits from the CA.

In some embodiments, each CA may be assigned a section of audio and thecorresponding portion of the reference transcription to correct.Additionally or alternatively, the CAs may take turns editing the samesegment of the reference transcription.

As another example, the environment 4700 may not include thetranscription unit 4730. In these and other embodiments, the texteditors 4742 may serve as an error labeling tool to enable the CAs toread the hypothesis transcription, listen to the corresponding audio,and mark and/or count errors. In these and other embodiments, the texteditors 4742 may count errors using input from the CAs. Alternatively oradditionally, the text editors 4742 may be configured to present one ormore of: (1) the reference transcription, (2) the hypothesistranscription, (3) marks and scores from other CAs, and (4)automatically marked errors displayed as, for example, an aligned errormap, each in a normalized and/or denormalized form.

Environments for determining accuracy and/or scoring of transcriptionunits as described with reference to FIGS. 44-47 may also be used tomeasure one or more of: word accuracy, capitalization accuracy,punctuation accuracy, and other forms of accuracy. Alternatively oradditionally, the reference transcription generated in the environmentsof FIGS. 44-47 may be sent as a transcription or correction to a userdevice when the audio is part of a communication session in which theuser device is participating or associated therewith or the audio isprovided by the user device. The user device may display thetranscription or correction on the display and/or it may store it in astorage location such as a display buffer or audio record.

In some embodiments, the environment 4700 may be configured to deleteaudio when the audio is complete, such as when the audio is from acommunication session and the communication session ends or whentranscriptions are completed and delivered, in response to laws,regulations, and other policies which may prohibit the archival of suchaudio.

FIG. 48 illustrates an implementation of an audio delay 4800, inaccordance with some embodiments of the present disclosure. The audiodelay 4800 may be configured to delay audio based on a determined delaytime using an audio buffer 4802 and output the delayed audio.

In some embodiments, the delay time may be determined by the audio delay4800 from endpoints obtained from an ASR system. In some embodiments,the audio delay 4800 may obtain endpoints. Alternatively oradditionally, the audio delay 4800 may generate endpoints from audio andtext. As described above, at least with reference to FIGS. 1, 31, 32, 33a, 33 c, 39, 40, 41, and 42, that illustrate audio delay, endpoints maybe used to determine a delay time. If endpoints are not available, theaudio delay 4800 may be configured to generate the endpoints.

In some embodiments, the audio delay 4800 may include an ASR system4820. In these and other embodiments, the ASR system 4820 may obtainaudio and a transcription of the audio. Using the audio and thetranscription of the audio, the ASR system 4820 may be configured todetermine a set of endpoints that correspond to the best alignmentbetween the text and the audio. In these and other embodiments, the ASRsystem 4820 may obtain both the transcription and the audio. The ASRsystem 4820 may recognize both the audio and the transcription as an ASRconstraint. For example, the text may be used to create a grammar orlanguage model for the ASR system 4820. Using both the audio and thetranscription, the ASR system 4820 may determine the locations in theaudio that correspond with words in the transcription and thus maydetermine a set of endpoints that correspond to the best alignmentbetween the transcription and the audio. The endpoints may betransmitted to the audio buffer 4802. The audio buffer 4802, in someembodiments, may be configured to determine a delay time setting basedon the endpoints, for example by subtracting the average time that wordsin a transcription are generated from the average time the words appearin the input audio. Modifications, additions, or omissions may be madeto the audio delay 4800 without departing from the scope of the presentdisclosure.

FIG. 49 illustrates an example environment 5300 for measuring accuracyof a transcription service, in accordance with some embodiments of thepresent disclosure. In some embodiments, the environment 5300 includes atranscription monitor 5302 that includes a signal interceptor 5304, acamera 5306, and an auxiliary pad 5308.

In some embodiments, a first user device 5352 may establish acommunication session with a second user device 5350. The first userdevice 5352 may obtain communication session audio and may transmit thecommunication session audio to a transcription service 5312 which mayuse any of the transcribing configurations described herein to generatea transcription of the communication session audio. The transcriptionservice 5312 may provide the transcription to the first user device 5352for display. In some embodiments, the transcription may be displayed onthe first user device 5352 or the auxiliary pad 5308. In someembodiments, the auxiliary pad 5308 and signal interceptor 5304 may becomputers such as smartphones, desktop, notebook, laptop, embedded, ortablet computers, or computers incorporated into other householdappliances including, but not limited to, a TV, a voice-controlledspeaker or smart home speaker, a refrigerator, a car dashboard display,a network router, a wall display or another display in another location.

Signals in the transcription monitor 5302, including communicationsession audio, transcriptions and other information provided on the userdevice, signals from input of a user, communication session statusinformation, information on selections and other action taken by theuser, such as turning captioning on or off, and Internet or networktraffic to and from the first user device 5352 may be captured by thesignal interceptor 5304 and transmitted to an accuracy measurementservice 5316.

For example, in some embodiments, the signal interceptor 5304 may beconfigured to capture communication session audio from one or bothdevices in the communication session. For example, the first user device5352 and the communication network 5314 may connect to the signalinterceptor 5304, which may provide a path between the first user device5352 and the communication network 5314. In these and other embodiments,the signal interceptor 5304 may capture audio passing therethrough andmay transmit captured audio to an accuracy measurement service 5316.Additionally or alternatively, the signal interceptor 5304 may includeXLR input and output jacks connected together through the signalinterceptor 5304 with a tap in the communication line so that the signalinterceptor 5304 may extract a copy of one or more audio signals on thecommunication line. Alternatively or additionally, the signalinterceptor 5304 may use an echo canceler or other source separationmethod to eliminate any crosstalk and separate audio from the seconduser device 5350 so that audio originating at the first user device 5352appears on a first channel and audio originating at the second userdevice 5350 appears on a second channel. The signal interceptor 5304 maytransmit the audio on the second channel to the accuracy measurementservice 5316.

The arrangement of the signal interceptor 5304 shown here, wherecommunication session audio passes through the signal interceptor 5304,is illustrative only, and other configurations are contemplated. In oneexample, the first user device 5352 may transmit communication sessionaudio to the signal interceptor 5304 using a separate connection such asvia a LAN (local area network), WiFi, Bluetooth, or a separate wiredconnector. Alternately or additionally, the signal interceptor 5304 maytap into the communication line using a “T” connection or inline audiointerface such as a telephone audio tap so that the communication signalmay be copied to, but not pass through, the signal interceptor 5304. Inanother example, the signal interceptor 5304 instead of sitting in-linethrough the communication line as illustrated, may sit in-line through ahandset cord of the first user device 5352, passing audio signals inboth directions and capturing audio from one or both parties. An exampleof a telephone audio tap may include a device that is inserted in-linein a handset cord or a phone line cord using two telephone connectorsthat completes the circuit between the two connectors so that telephoneoperation is unaffected by the insertion. The telephone audio tap maysend a copy of audio from one or both ends of the conversation toanother device such as the signal interceptor 5304.

In some embodiments, the signal interceptor 5304 may capture Internet ornetwork traffic passing to or from the first user device 5352. Thecaptured network traffic may include messages, audio, and transcriptionsto and from the transcription service 5312. Network traffic may becaptured, as shown, by passing through the signal interceptor 5304;however other arrangements are contemplated. For example, the signalinterceptor 5304 and the first user device 5352 may connect to a network5301, such as by connecting into the same router. In these and otherembodiments, the router may transmit a copy of network traffic passingbetween the first user device 5352 and the transcription service 5312 tothe accuracy measurement service 5316. Alternately or additionally, thefirst user device 5352 may transmit information, such as at least someof the information contained in the network traffic, to the signalinterceptor 5304. Alternately or additionally, signals used by theaccuracy measurement service 5316 to measure accuracy may be obtained byother mechanisms. For example, accuracy measurement service 5316 mayobtain audio from a connection to the first user device 5352 via amicrophone and obtain transcriptions from the camera 5306.

In some embodiments, the signal interceptor 5304 may capture video fromthe camera 5306 and transmit the video to the accuracy measurementservice 5316. In these and other embodiments, the camera 5306 may beconfigured to view the first user device 5352 display so that video oftranscriptions appearing on the display is transmitted to the accuracymeasurement service 5316. The camera 5306 may capture other displayedinformation such as the identity or ID number of the CA that may beassisting with the transcription being displayed, whether thetranscription is being performed by a revoicing or non-revoicingtranscription unit, communication session status, identifications of thesecond user device 5350, and other information that may appear on adisplay of the first user device 5352. Additionally or alternatively,the signal interceptor 5304 may be configured with OCR (opticalcharacter recognition) to convert video transcriptions and otherdisplayed information to text and may transmit displayed information astext or other messages to the accuracy measurement service 5316.Alternatively or additionally, the camera 5306 may be further configuredto view one or more of: controls of the first user device 5352 such asbuttons, switches, and dials; other devices connected to the first userdevice 5352, and a user of the first user device 5352. For example, insome embodiments, the transcription monitor 5302 may use a signal fromthe camera 5306 watching the user's eyes to determine, for example,where the user is looking, the identity and other visual characteristicsof the user, when the user is watching transcriptions on the first userdevice 5352, and when the user is watching transcriptions on a displayof an auxiliary pad 5308 or other display.

In some embodiments, the camera 5306 may include mounting hardware tohold the camera 5306 in a position capable of viewing the display of thefirst user device 5352. In these and other embodiments, the mountinghardware may connect the camera 5306 to the first user device 5352. Forexample, the camera 5306 may be mounted on the edge or side of the firstuser device 5352 and may view the screen from the top, side, or bottom.If the camera 5306 view is at an angle such that the screen image isdistorted, an image correction filter may be used to compensate for theangle and convert the screen image into a rectangular shape or anotherformat more easily read by or compatible with OCR mechanisms. An imagecorrection filter may also be used to remove glare or reflections fromthe screen. The camera 5306 may include an indicator light to indicatewhen the camera is active.

In some embodiments, the camera 5306 may, for example, be built into thesignal interceptor 5304, first user device 5352, or auxiliary pad 5308or it may connect to the signal interceptor 5304 via a wired connection,a connection through the first user device 5352, a connection throughthe auxiliary pad 5308, a wireless connection such as Wi-Fi, Bluetoothor cellular, or a network connection such as a LAN connection through arouter.

In some embodiments, the transcription monitor 5302 may also beconfigured to provide transcriptions, which may be generated by theaccuracy measurement service 5316, based on the communication sessionaudio. The transcriptions may be displayed on the auxiliary pad 5308.Additionally or alternatively, the transcriptions may be displayed onthe first user device 5352. These transcriptions may be in addition totranscriptions provided by the transcription service 5312.

Additionally or alternatively, the transcription monitor 5302 may beconfigured to record communication session data including communicationsession audio, transcriptions such as transcriptions from thetranscription service 5312 of the communication session audio, andrecord other communication session data such as IM or text messages sentbetween the first user device 5352 and the second user device 5350.

Alternatively or additionally, the communication session data capturedby the transcription monitor 5302 may include: audio, text,transcriptions, punctuation, capitalization, communication session loginformation such as phone numbers, a model built using content (e.g.,“on-the-fly” in case consent is declined), and statistics such asn-grams derived from communication session data. In some embodiments,the communication session data may be used to train models, such as ASRmodels, confidence models, capitalization models, and punctuationmodels, and to determine values of one or more parameters. Additionallyor alternatively, models may be used, for example by an ASR system, toprovide transcriptions (a) for the current communication session, (b) tocaption future communication sessions, and (c) to transcribe audio fromcallers other than the caller from which communication session data wascollected. Additionally or alternatively, the transcription monitor 5302may extract statistics from communication session data. The statisticsmay include n-grams, n-gram counts, usage such as minutes of use, andanalysis of topics of conversation.

In some embodiments, the transcription monitor 5302 may be configured toobtain consent from one or more parties of the communication sessionprior to intercepting communication session data. If consent is neededto perform one or more of the above actions, one or more methodsdescribed in this disclosure may be used to collect and store theconsent. The transcription monitor 5302 may be configured to receiveinput from a user, such as the subscriber, indicating consent to havethe communication session recorded. Examples of mechanisms forrequesting and recording consent include, but are not limited toproviding consent by signing a form, going to a website, talking to anIVR or representative of the captioning service, clicking a screen icon,or by pressing a button. The user may be asked to provide consent forone or more of the actions listed above, such as via an audio prompt, arequest on a phone or other display, an IVR system, a transcriptionservice representative on the communication session, or verbally byanother calling party. Consent provided by a user may apply to the userand to one or more other parties in the communication session. Forexample, the user may be prompted to affirm that he/she provides consentfor himself/herself or for all parties on the communication session.When providing consent for another calling party, the user may beprompted to ask the other party for consent. The user may also indicatethat consent is denied.

In some embodiments, the consent may be obtained through a physicalbutton, a virtual button or soft key on a display, a touch tone detectoror an ASR system that accepts a DTMF or voice command, an option on aweb site, or another form of input such as typing a phrase such as asignature or an affirmative phrase such as “yes.”

Upon receiving or being denied consent, a record of the user and consentdecision may be stored in a consent database, which may be part of adata store 5318. A consent detector may determine whether a given actionmay be performed with data from a given calling party.

Communication data obtained from the signal interceptor 5304 may be,contingent on consent, stored in the data store 5318 and/or processed bythe accuracy measurement service 5316. In some embodiments,transcriptions may be provided from the accuracy measurement service5316 on a display such as a display of the auxiliary pad 5308,regardless of consent. Additionally or alternatively, generating thetranscriptions by the accuracy measurement service 5316 may becontingent on consent and transcriptions may appear on the display ofthe auxiliary pad 5308 after consent has been granted. In someembodiments, the auxiliary pad 5308 may display text for a consentrequest and a virtual button or soft key to be pressed, clicked, orselected, indicating that the user grants consent. In some embodiments,the transcription monitor 5302 may provide the communication sessiondata to the accuracy measurement service 5316. The accuracy measurementservice 5316, in some embodiments, may include a router 5340, the datastore 5318, a reader 5320, a driver 5322, an audio transcriber 5324, anda scorer 5332. The communication session data may be received from thenetwork 5301 by the router 5340. In some embodiments, the communicationsession data may be stored in the data store 5318.

In some embodiments, the accuracy measurement service 5316 may beconfigured to compute accuracy of the transcriptions generated by thetranscription service 5312 in real time, such as during thecommunication session for which the transcriptions are generated.Accuracy may be measured by comparing a reference transcription to ahypothesis transcription. The hypothesis transcriptions maysubstantially correspond to transcriptions displayed on the first userdevice 5352. The hypothesis transcriptions may be obtained using one ormore methods, including:

-   -   1. The reader 5320 analyzes video from the camera 5306 to        extract a hypothesis transcription using optical character        recognition (“OCR”). OCR may be performed by the transcription        monitor 5302, the accuracy measurement service 5316, or by an        OCR service reachable via an API.    -   2. One or more displays showing the camera video and one or more        text editors that allow text creation and editing may provide        mechanisms for data entry people to enter a text transcription        of the transcriptions visible in the video signal. The data        entry people may enter and edit text using keyboard, voice, or        other computer input methods. To perform the data entry        accurately and in real-time, the reader 5320 may use an        arrangement such as those illustrated in FIG. 46, except that        data entry people may view images as input instead of listening        to audio. The text editor may display a video and may allow data        entry people to forward and rewind the video.    -   3. The first user device 5352 may transmit a message to the        reader 5320 that includes transcriptions that may be used as the        hypothesis transcriptions.    -   4. The transcription service 5312 may transmit a message to the        reader 5320 that includes transcriptions.    -   5. Transcriptions may be extracted from network traffic passing        to or from the first user device 5352. Network traffic or        transcriptions may be read and transmitted to the reader 5320 by        the signal interceptor 5304.

In some embodiments, the audio transcriber 5324 may be configured toconvert the communication session audio obtained by the accuracymeasurement service 5316 to a reference transcription. The audiotranscriber 5324 may use any of the systems and/or methods discussed inthis disclosure to generate the reference transcription. The referencetranscription may serve as the “truth” in measuring accuracy of thehypothesis transcription generated by the reader 5320 based on thetranscription generated by the transcription service 5312.

Alternatively or additionally, in some embodiments, the audiotranscriber 5324 may use the transcriptions from the transcriptionservice 5312 in creating the reference transcription. For example, thereference transcription may be used as a starting point to be edited byhuman editors using text editors. Additionally or alternatively, thetranscriptions from the transcription service 5312 may be fused withother transcriptions, such as those generated by the audio transcriber5324 based on the communication session audio to create a referencetranscription.

In some embodiments, the audio transcriber 5324 may provide thereference transcription to the driver 5322. The driver 5322 may beconfigured to format the reference transcription for display to the userand transmit the formatted reference transcription to the transcriptionmonitor 5302. The transcription monitor 5302 may present the formattedreference transcription on a display such as on the display of theauxiliary pad 5308. The driver 5322 may be configured to format thereference transcriptions by breaking the reference transcriptions intogroups of words which may be presented substantially simultaneously onthe display. In some embodiments, the transcriptions provided by theaccuracy measurement service 5316 may not include the referencetranscription but may be a transcription used to create the referencetranscription.

In some embodiments, the reference and hypothesis transcriptions may becompared by the scorer 5332 to generate a real-time score. After thereal-time score is determined, communication session data such asreference and hypothesis transcriptions, audio, and video, may bedeleted. The real-time score may be stored and analyzed. For example,the real-time score may be averaged over multiple communication sessionsto determine an average accuracy for the transcription service 5312. Aswith other real-time accuracy estimation systems disclosed in thisdisclosure, the accuracy of the accuracy measurement service 5316 may beverified and tuned by sending recorded and transcribed audio through theaccuracy measurement service 5316 and comparing the estimatedtranscriptions and accuracy figures determined in real time to accuracyfigures determined offline. Additionally or alternatively, a correctedreal-time accuracy may be obtained by comparing the offline accuracywith accuracy determined using recorded and transcribed audio todetermine a correction factor or method to adjust the accuracy output bythe scorer 5332.

In some embodiments, the accuracy measurement service 5316 may beconfigured to determine accuracy estimates using offline or recordedaudio. For example, the accuracy measurement service 5316 may beconfigured to use recorded data in the data store 5318 as input to thereader 5320 and audio transcriber 5324. If recorded audio is alreadytranscribed, the audio transcriber 5324 may be omitted/bypassed and thetranscribed recorded audio may be used as the reference transcription.

Modifications, additions, or omissions may be made to the environment5300 without departing from the scope of the present disclosure. Forexample, in some embodiments, the transcription monitor 5302 may be oneunit. Alternatively or additionally, the signal interceptor 5304 may bea collection of separate units such as a first unit for processing androuting signals and a second unit for capturing audio. Additionally oralternatively, components including the signal interceptor 5304 mayshare hardware with one or more other components of the transcriptionmonitor 5302, including the auxiliary pad 5308 and the camera 5306. Thearrangement of the transcription monitor 5302, with elements distributedbetween the signal interceptor 5304, camera 5306, and auxiliary pad 5308is provided as an example. Other arrangements are contemplated,including an arrangement where components of the transcription monitor5302 are integral with the first user device 5352. The division ofcomponents between the transcription monitor 5302 and the accuracymeasurement service 5316 is also an example embodiment. In someembodiments, components of the transcription monitor 5302 and theaccuracy measurement service 5316 may each reside in any of multiplelocations. For example, components of the accuracy measurement service5316 may reside in the transcription monitor 5302 and vice versa.

In some embodiments, the auxiliary pad 5308 may include a display and acamera. Alternatively or additionally, the auxiliary pad 5308 may beconfigured to interface with a display and a camera. For example, theauxiliary pad 5308 may provide input to a display and/or may becommunicatively coupled to a camera. In some embodiments, the auxiliarypad 5308 may be configured to obtain consent from a user. Additionallyor alternatively, the auxiliary pad 5308 may interface with an audio tapand a camera and may incorporate at least some of the functionsdescribed above for the transcription monitor 5302.

FIG. 50 illustrates an example environment 5500 for measuring accuracy,in accordance with some embodiments of the present disclosure. In someembodiments, communication session audio received at a transcriptionunit 5514 in real time from a real-time communication session may onlybe available for a brief period, such as for the duration of thecommunication session, before being deleted. In some circumstances, lawsand/or regulations may prohibit recording or storage of thecommunication session audio longer than the duration of thecommunication session. Accordingly, measuring accuracy of atranscription generated from the communication session audio may alsooccur before the communication session audio is deleted. In the exampleenvironment 5500, the transcription unit 5514 may generate a hypothesistranscription in real time or substantially real-time. In someembodiments, the transcription unit 5514 may be configured in any mannerdisclosed in this disclosure. The hypothesis transcription may also bescored in real-time or substantially real-time by a scorer 5520configured to determine an estimated accuracy of transcriptionsgenerated by the transcription unit 5514.

In some embodiments, the communication session audio and/or thehypothesis transcriptions may be recorded by a data store 5504. In theseand other embodiments, the communication session audio may betranscribed offline by an offline transcription tool 5522 and stored inthe data store 5504.

In some embodiments, the scorer 5520 may be used to determine anestimated accuracy of the hypothesis transcription generated by thetranscription unit 5514. Additionally or alternatively, the scorer 5520may be used to determine accuracy of components included within thetranscription unit 5514. For example, in some embodiments, thetranscription unit 5514 may include a revoicing ASR system to transcriberevoiced audio from a CA, other ASR systems, one or more fusers, and oneor more text editors, among other components. The scorer 5520 may beused to determine accuracy of a particular revoicing ASR systemassociated with the CA, one or more of the components included with thetranscription unit 5514, or both. In these and other embodiments, theaccuracy of the revoicing ASR system associated with the CA may be usedas a proxy that reflects the accuracy of the CA. Alternatively oradditionally, a transcription generated by the particular ASR system andas corrected by a text editor associated with the CA may be a proxy thatreflects the accuracy of the CA. In these and other embodiments, a CAmay be compared to another CA based on the accuracies generated bytranscription units associated with each of the CAs. In these and otherembodiments, the transcription units may be configured in an analogousmanner except the CA profiles used by the transcription units may bedifferent as the CA profiles used may be selected based on the CAs beingcompared.

To determine an estimated accuracy, the scorer 5520 may operate in asupervised mode or an unsupervised mode. In a supervised mode, thescorer 5520 may compare a hypothesis transcription to a supervisedreference transcription, such as a reference transcription from the datastore 5504, count the number of disagreements, and determine anestimated accuracy. The supervised mode may use, for example, the methoddescribed for the scorer in FIG. 22, among other figures. In anunsupervised mode, the scorer 5520 may use an alternate method, one thatmay not use a supervised reference transcription in the manner used bythe supervised mode, to determine an estimated accuracy. Theunsupervised mode may use a selector or accuracy estimator such asdescribed with reference to FIGS. 18-21, 23, 24-27 b, 45, and 46.

In some embodiments, the scorer 5520 may be used in a process todetermine an estimated accuracy of the hypothesis transcription thatuses both the supervised mode and the unsupervised mode. In these andother embodiments, the process may include the scorer 5520 determiningan estimated accuracy and determining a calibration factor that may beused to adjust the estimated accuracy. An example of the process mayinclude the following:

-   -   1. A calibration audio set of multiple audio samples for which        consent to record and process has been obtained, is stored in        the data store 5504.    -   2. The calibration set may be transcribed using an offline        transcription tool 5522, which enables a human transcriber to        listen to the audio and create reference transcriptions. If        transcriptions exist for the audio sample, the offline        transcription tool 5522 may be used by the human transcriber to        correct errors in the transcriptions. In addition to using        audio, the offline transcription tool 5522 may use text and rich        text forms such as a word confusion network (WCN), n-best list,        and lattice output from an ASR system to generate the reference        transcriptions. For example, the offline transcription tool 5522        may use rich text forms to provide multiple hypotheses that a        human transcriber may select to correct the transcriptions.    -   3. The reference transcription may be denormalized.    -   4. The data store 5504 may send the audio samples to the        transcription unit 5514.    -   5. The transcription unit 5514 may generate a hypothesis        transcription for each of the audio samples.    -   6. The hypothesis transcriptions from the transcription unit        5514 may be denormalized.    -   7. Using the supervised mode, the scorer 5520 may compare the        hypothesis transcriptions from the transcription unit 5514 to        the reference transcriptions to determine a target accuracy for        each audio sample in the calibration audio set.    -   8. Using the unsupervised mode, the scorer 5520 may evaluate the        hypothesis transcription from the transcription unit 5514 to        determine an estimated accuracy for each audio sample in the        calibration audio set.    -   9. The unsupervised mode of the scorer 5520 may be trained,        tested, or calibrated using, for example, one or more of the        following methods:        -   a. (Train) A machine learning method such as one from Table            9 is used to train an accuracy estimator, accuracy            correction estimator, or selector used by the scorer 5520. A            cost function used for training may be chosen and model            parameters may be selected to reduce the difference between            the estimated accuracy and the target accuracy.        -   b. (Test) The target accuracy for the scorer 5520 may be            compared to the estimated accuracy. The comparison may be            used to determine whether the estimate is sufficiently close            to the target to meet specified requirements.        -   c. (Calibrate) The estimated accuracy for the scorer 5520            may be subtracted from the target accuracy to determine how            different the estimate and target are, and in which            direction. The difference may be used to set parameters in            the scorer 5520 or to calculate a correction factor (see            FIG. 23) to be applied to the estimated accuracy generated            by the scorer 5520.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations, or expanded into additional operationswithout detracting from the essence of the disclosed embodiments.

An example of operation of the scorer 5520 is now provided. The scorer5520 may use the process described above to train an accuracy estimator.A transcription may be obtained by the scorer 5520 from thetranscription unit 5514. The transcription may be applied to theaccuracy estimator to determine an initial estimated accuracy of thetranscription. A correction factor generated previously by the scorer5520 may be applied to the initial estimated accuracy to generateestimated accuracy of the transcription.

In some embodiments, the scorer 5520 may also be used to calibrate,train, or validate itself or other scorers or selectors. Alternativelyor additionally, the scorer 5520 may be configured to score andbenchmark the transcription unit 5514. In these and other embodiments,the environment 5500 may be configured to monitor overall accuracy ofthe transcription unit 5514 or other transcription units. In someembodiments, accuracy monitoring may be used to (see also Table 14):

-   -   1. Provide data to dashboards for viewing operations status such        as accuracy, automation rates, traffic volumes, and system        resources.    -   2. Raise alerts for identifying development bugs and problems in        the field such as outages or equipment failures.    -   3. Confirm that software, hardware, and model updates have been        deployed correctly.    -   4. Analyze changes in user behavior that affect accuracy or        automation rates.    -   5. Track individual or average CA performance and identify        training or personnel issues.

Modifications, additions, or omissions may be made to the environment5500 without departing from the scope of the present disclosure. Forexample, in some embodiments, the steps to calibrate, train, or validatethe scorer 5520 may also be used to calibrate, train, or validate aselector by using estimated and target transcription unit selection inplace of estimated and target accuracy, a selector in place of thescorer 5520, and a threshold (see FIG. 19) in place of the correctionfactor.

FIG. 51 illustrates an example environment 5600 for testing accuracy oftranscription units, in accordance with some embodiments of the presentdisclosure. In some embodiments, the environment 5600 may be configuredto test accuracy and create and/or update a denormalization equivalencytable. For example, when no equivalency table exists, the environment5600 may create an initial equivalency table. Where an equivalency tabledoes exist, the environment 5600 may use the equivalency table forscoring, reviews, and audits. The environment 5600 may update theequivalency table to correct errors and add new equivalencies.

In some embodiments, the environment 5600 may include a revoicing pool5602 that includes one or more revoicing transcription units 5644. Theenvironment 5600 may also include a non-revoicing pool 5604 that mayinclude one or more non-revoicing transcription units.

The environment 5600, in some embodiments, may include an equivalencyreview tool 5606 that may be configured to create, maintain, and updatean equivalency table 5608. An equivalency table 5608 may include tokenequivalencies that may be used by denormalizers 5630 when denormalizinga transcription. For example, the equivalency table 5608 may specifythat “1” is the same as “one,” but that “they're” is not the same as“their.” The environment 5600 may include a different equivalency table5730 for different ASR systems in the different pools 5602 and 5604 or asingle equivalency table 5730 for the environment 5600.

In some embodiments, the environment 5600 may be configured to testaccuracy of transcription units and create the equivalency table 5608.The data for the testing may be based on: prerecorded audio capturedfrom people/actors in a studio; people connected to a data collectionsystem by, for example, calling or receiving communication sessions froma specified phone number or running a PC-based or smartphone-basedapplication; production traffic (i.e., real phone communicationsessions) processed by a transcription service; and traffic from acommunication service where audio is created.

In some embodiments, a transcription or editing tool 5610 with audioplayback enables a transcriptionist to create a reference transcription.In these and other embodiments, the tool may use an ASR system to createan initial transcription for editing by the transcriptionist. Thetranscriptionist may edit the transcription using a computer keyboard,touch screen, voice input, or other computer interface device. Ifrecording of audio is allowed (i.e., local law and regulations allowrecording), audio and transcriptions may be saved in a reference datadatabase 5612. Where recording of audio is not allowed, audio andtranscriptions may be processed by the environment 5600 illustrated hereand then deleted when a communication session is completed.

In some embodiments, the environment 5600 also includes a scheduler 5614configured to receive input test requests from an operator (a person).Requests may include how many tests to run, when tests should becomplete, types of tests to be run, which transcription units 5644should be tested, which CAs should be tested using associatedtranscription units, and under what conditions to run tests. Thescheduler 5614 may be responsive to test requests to generate a set oftest parameters, which may include when to run tests, which audio filesto use for testing, a schedule for which transcription units 5644 totest, and how many tests to run simultaneously. The scheduler 5614 mayquery or receive input from a transcription unit scheduling system orother operations and administration systems to determine operationsstatus such as transcription unit load, traffic load, transcription unitavailability, and may alter test parameters to avoid interfering withthe transcription of audio from live communication sessions when thetranscription units may be part of a transcription service. Additionallyor alternatively, the scheduler 5614 may run tests on demand from anoperator or team lead supervisor (“TLS,” a.k.a. CA manager) based onreceived requests.

In some embodiments, the environment 5600 may also include an automatedtranscription accuracy and latency testing (“ACALT”) tool 5616configured to retrieve an audio sample from the reference data database5612 and send it to one or more transcription units in either or boththe revoicing pool 5602 or the non-revoicing pool 5604. The ACALT 5616may send an audio file to multiple transcription units to obtainmultiple transcriptions for use in (a) testing multiple transcriptionunits or (b) fusing the results into a reference transcription orhigher-accuracy transcription. The multiple transcriptions from thetranscription units may be provided to the ACALT 5616. The ACALT 5616may designate the transcriptions as hypothesis transcriptions. Thehypothesis transcriptions may be stored in a trial results database5618.

In some embodiments, the ACALT 5616 may send the audio to atranscription unit and receive the transcription through any of severalmechanisms, including:

-   -   1. The ACALT 5616 sends a transcription request directly to a        transcription unit.    -   2. The ACALT 5616 sends a transcription request to an automatic        communication session distributor (“ACD”) 5640. The ACD 5640 may        find and create a connection to an available transcription unit        or, if instructed by the ACALT 5616 or the scheduler 5614,        connect to a specified transcription unit or to a transcription        unit associated with a specified CA.    -   3. The ACALT 5616 sends the transcription request to the pools        5602 and 5604.    -   4. The ACALT 5616 sends a request to a first phone to place a        communication session, for example via a network, to a second        phone, which may be a transcription phone. The ACALT 5616 then        plays audio to the first phone. Audio travels over the telephone        network to the transcription phone which sends audio to a        transcription unit for transcription. The transcription returns        to the transcription phone. The ACALT 5616 reads transcriptions        from the transcription phone, for example via a digital        connection or by reading the screen with a camera.

In some embodiments, a scorer 5650 may read a reference transcriptionfrom the reference data database 5612 and a hypothesis transcriptionfrom the trial results database 5618. The scorer 5650 may usedenormalizers 5630 to denormalize the reference and hypothesistranscriptions. The denormalizers 5630 may make use of the equivalencytable 5730 or other denormalization methods such as a set of rules fordetermining when two forms of the same token represent the same token.Additionally or alternatively, a reference transcription may be markedwith allowable equivalencies or alternative forms. For example, if areference reads “I {want/wanted} to give you a call,” a hypothesistranscription may be considered correct if it contains either “want” or“wanted.”

The denormalizers 5630 may be used in cases where the format of multipletranscriptions, such as the reference and hypothesis transcriptions, maybe different. Throughout this disclosure, it is to be understood thatthe denormalizers 5630 may be omitted if a transcription is already in aformat suitable to its use. For example, if a transcription from atranscription unit has not been normalized or if the transcription hasalready been denormalized, denormalization may be unnecessary.Denormalization may also be unnecessary when comparing or fusingtranscriptions that are in a similar format, or when the transcriptionwas already denormalized by a previous step such as by a fuser thatincludes a denormalizer.

Additionally or alternatively, when a transcription has been processedby a component including internal or implicit denormalization logic(e.g., components that perform aligning, fusing, voting, privacyfiltering, matching, scoring, etc.), the denormalizers 5630 may bedisabled or omitted. For example, the comparer in FIG. 45, and thecomponents performing the functions of alignment, voting, and countingerrors in component 5620 may include access to an equivalency table orother methods configured to handle equivalencies so that the componentsmay detect when different forms of the same word or phrase areequivalent. Additionally or alternatively, the depicted embodiment mayuse the equivalency table 5608 for denormalization by the denormalizer5630. However, other configurations may be used for other forms ofdenormalization, such as those based on sets of rules, data-drivenmethods using machine learning, etc.

In some embodiments, the scorer 5650 may be configured to align andcompare the reference transcription and the hypothesis transcription.The depicted embodiment, for the sake of clarity, has combined thefunctions of multiple previously described (see FIGS. 22, 44, and 45,for example) components into the component 5620. The align, compare, andcount errors component 5620 aligns and compares the transcriptions andcounts the number of differences and determine one or more accuracymetrics, which are then used for reports sent to a CA's team leadsupervisor (TLS), feedback, benchmarking, etc. Additionally oralternatively, the scorer 5650 may be configured, via thealign/compare/count errors component 5620, to create an error map, orrepresentation of differences found between the reference and hypothesistranscriptions, and transmit the error map to a TLS tool 5622.

The TLS tool 5622, in some embodiments, may be configured to displayscores and error maps to a TLS. The TLS tool 5622 may indicate when atest is ready to be scored, test score statistics, what pending testsneeds to complete, and results from tests. The TLS tool 5622 may givethe TLS several options, including:

-   -   1. Approve the automated score to be used for feedback and other        reports.    -   2. Review the automated score and make corrections before it is        used for feedback and other reports.    -   3. When automated scoring counts a word or phrase in a        hypothesis transcription as an error, the TLS may determine the        automatically detected difference is not an error. For example,        suppose the speaker says, “I wanted to remind you” and the        transcription unit transcribes the phrase as “I want to remind        you.” The TLS may determine the meaning of the phrase is not        changed or the audio was unclear and decide that the difference        is not to be counted as an error.    -   4. Propose that certain equivalencies be included in or removed        from the equivalency table, equivalency rules, or other        denormalization methods. The proposal may include a recitation        of a context (e.g., neighboring words) in which the proposed        equivalency applies. Using the example above, the TLS may submit        a request that “I wanted to” transcribed as “I want to” not be        counted as an error during automatic scoring, but that “wanted”        may not necessarily be considered equivalent to “want” in all        contexts.

Proposed equivalencies, along with optional context, that the TLS deemsare equivalent may be entered in a proposed equivalency database 5624,by the TLS tool 5622. The entry may include other information, includinga reference to a communication session where the equivalency may apply.The equivalency database 5624 may also track request statistics such ashow many TLSs proposed a given equivalency change, how many times thegiven equivalency change was proposed, how often the term occurs inproduction transcriptions, how many times TLSs indicated a difference asan equivalency, and whether the equivalency has been previouslyconsidered and accepted or rejected.

The equivalency review tool 5606 may be configured, in some embodiments,to allow a scoring manager to review proposed equivalencies and acceptor reject them. The equivalency review tool 5606 may be configured toremove duplicate requests and may display request statistics.Additionally or alternatively, the equivalency review tool 5606 may helpa scoring manager prioritize which proposed equivalencies to reviewfirst by displaying request statistics or using request statistics tosort requests. For example, the equivalency review tool 5606 may allow ascoring manager to find and review proposed equivalencies that havenever been reviewed before and have been submitted by at least a minimumnumber of TLSs.

In some embodiments, in response to a scoring manager accepting anequivalency, the equivalency may be incorporated into the equivalencytable 5608. If the scoring manager rejects an equivalency, it may recordthe rejection in a database of rejected pairs 5626. When a TLS takes aninitial step (such as clicking on one of the terms in an equivalencypair) to determine a difference is an equivalency that automated scoringdid not recognize or when the TLS takes an initial step in submitting aproposed equivalency request, the TLS may be advised, such as with apop-up or alert message embedded in a credit or submission window (awindow that allows the TLS to give a CA credit for an incorrectlyassigned error, submit a proposed equivalency, etc.), when anequivalency has been previously rejected. Depending on the reviewhistory of a proposed equivalency and on policies implemented in theequivalency review tool 5606 and the TLS tools 5622, the TLS tools 5622may bar the TLS from giving credit and/or from submitting a request.Modifications, additions, or omissions may be made to the environment5600 without departing from the scope of the present disclosure. Forexample, a single equivalency table 5608 is illustrated for use by bothdenormalizers 5630 a and 5630 b, but in some embodiments, multipleequivalency tables may be used. In these and other embodiments, eachdenormalizer 5630 a and 5630 b may use a different equivalency table.

FIG. 52 illustrates an example environment 5700 for equivalencymaintenance, in accordance with some embodiments of the presentdisclosure. The environment 5700 may be configured for generating,approving, editing, and compiling equivalencies. In the depictedembodiment, a denormalizer 5720 may utilize an equivalency table;however, other denormalization systems may be used.

In some embodiments, the denormalizer 5720 may include a preprocessor5702, an equivalency converter 5704, and a postprocessor 5706.Normalized text may be received by the preprocessor 5702, which may makechanges that apply broadly or that are not well mapped in an equivalencytable 5730, such as mappings not tied to specific words. Thepreprocessor 5702 may, for example, be configured to map non-standardcharacters to a usable form, eliminate HTML tags, correct spelling,strip out headers, remove or convert capitalization and punctuation,etc. The preprocessor 5702 may also be configured to convert namesfollowed by an apostrophe and “ll” or “s.” For example, the preprocessor5702 may map “Gary'll” to “Gary will” and “Gary's” to “Gary is” or “Garyhas.” The preprocessor 5702 may equate multiple forms of spelling forwords like “Cathy” that have multiple spelling options.

In some embodiments, the preprocessor 5702 may send converted text tothe equivalency converter 5704, which may be configured to convert termsto a uniform form using an equivalency table 5730. In some embodiments,a word or phrase matching a first term or regular expression in theequivalency table 5730 may be converted to a second term defined on thesame line of the table. One example of an equivalency table 5730 is a“GLM” file. The equivalency table 5730 may map terms to one or moreoptions in a format that the denormalizer 5720 recognizes and allows.For example, if the equivalency table 5730 includes the entry

-   -   Dr.=>{Doctor/Drive}        then “Dr.” may be converted to {Doctor/Drive}. During accuracy        scoring of transcriptions, a scorer may allow either “Doctor” or        “Drive” as a correct match to “Dr.” The reference transcription        may also be marked with multiple options. For example, when        creating a reference transcription, if it is uncertain whether        the audio included “hey,” “say,” or “hi,” then the reference        transcription may include “{hey/say/hi}” and the hypothesis        transcription may be considered correct in response to the        hypothesis transcription matching any of the three words. The        postprocessor 5706 may apply formatting or other desired        conversions before sending the transcription to be aligned,        compared, counted, etc. For example, the postprocessor 5706 may        convert to lower case from upper case.

In some embodiments, the equivalency table 5730 may be created orupdated by an equivalency review tool, such as an equivalency reviewtool 5606 of FIG. 51. Additionally or alternatively, the equivalencytable 5730 may be created from format rules 5726 by an equivalencycompiler 5708. In some embodiments, the equivalency review tool mayinclude approval tools 5705, viewing/editing and auditing tools 5707,and the equivalency compiler 5708. The format rules may include anynumber of specific rule groups including, but not limited to, possessivenouns 5710, abbreviations 5712, acronyms 5714, compound words 5716,contractions group 5718, colloquial words 5728, alternate spellings5722, filler words 5724, etc. A proposed equivalency 5709, such as froma TLS, may be reviewed using an equivalency review tool, which may allowthe proposed equivalency 5709 to be accepted or rejected. If theproposed equivalency 5709 is accepted, the proposed equivalency 5709 maybe added to a group of format rules. For example, if the proposedequivalency 5709 “there's”=“there is” is accepted, it may be added tothe contractions group 5718. A second set of tools allows a scoringmanager to view (including search, inspect, sort, detect potentiallyincorrect equivalencies), edit (including adding, modifying, or deletingequivalencies), and audit (review equivalencies for compliance withpolicies, check syntax) equivalencies.

In some embodiments, a scorer may treat filler words such as “urn,”“ah,” “hmm,” “hum,” and “mm” as regular words and count filler worderrors as having substantially equal weight as other words. Additionallyor alternatively, the scorer may ignore filler word errors and may notcount them against the total score. In other embodiments, the scorer maycount filler words as errors, but may allow the filler words to besubstituted with each other. For example, “uh” replaced by “ah” may notbe an error, but “him” replace by “hmm” may be an error. An ASR systemmay be trained to recognize filler words by, for example, includingsamples (such as audio and/or the corresponding text) in a trainingdatabase used to train ASR models used by the ASR system. In someembodiments, in a revoicing transcription unit, when revoicing fillerwords, the CA may speak the filler words, speaking a voice alias inplace of the filler word such as “udge” for “um,” typing the filler,using keyboard shortcuts, using a mouse or a gesture to select from alist of menu items, etc.

In some embodiments, the reference and/or hypothesis transcriptions mayinclude “quickwords” (i.e., audible events that may not appear as wordsin a dictionary and that may lack an established spelling). Examplesinclude “(beeping),” “(busy line),” “(coughing),” “(communicationsession is on hold),” “(crying),” “(dial tone),” “(fax tone),” “(gasp),”“(speaker is inaudible),” “(loud background noises interfering withcaptioning),” “(laughs),” “(music playing),” “(audio is cutting in andout),” “(speech in a language other than English or Spanish),”“(silence),” “(Spanish),” “(sigh),” “(sneezing),” “(static),” “(yawn),”etc. An ASR system may detect quickwords by modeling the sounds insimilar fashion to how phonemes are modeled, such as by mapping words orphoneme strings to quickwords, or by employing a separate classifierdesigned to detect quickwords, based on, for example text input and/oraudio analysis. In some embodiments, in a revoicing transcription unit,when revoicing audible events, the CA may enter quickwords into atranscription by typing them, using keyboard shortcuts, selecting from amenu, speaking the quickword or a voice alias such as “laughter” or“quickword yawn.” In some embodiments, quickwords may be counted orignored as described above for filler words.

In various accuracy scoring arrangements, such as those described abovewith reference to FIGS. 44-52, word accuracy may be measured by, forexample, deleting or ignoring punctuation and capitalization.Punctuation may be ignored, for example, by removing it in thepreprocessor 5702 so that it is not counted when computing the errorrate. Similarly, capitalization may be ignored by setting all text toupper (or lower) case in the preprocessor 5702 and/or postprocessor5706. Additionally or alternatively, punctuation and/or capitalizationmay be scored separately. For example, a scorer may determine a wordaccuracy score, a punctuation accuracy score, and a capitalizationaccuracy score. Additionally or alternatively, punctuation andcapitalization errors may be included in an overall accuracy score byinserting punctuation and capitalization tags into transcriptions beforescoring. For example, punctuation marks may be mapped to tokens in thetranscriptions. In these and other embodiments, the punctuation marksmay be mapped to characters or strings that are not expected to occurotherwise in the text. For example,

“.”→“_period_”

“,”→“_comma_”

“!”→“exclamation”

Continuing this example, the sentence, “Jacob's not sick, but Jane is.”may, for example, be rendered, for comparison and error rate calculationpurposes, as “Jacob is not sick_comma_but Jane is_period_.” Both thereference and the hypothesis transcriptions may be similarly convertedso that the reference and the hypothesis transcriptions may match whenthe hypothesis is correct. In this scenario, missing, adding, orsubstituting a punctuation mark, for example, may be scored as a worderror. Similarly, capitals are, in some embodiments, tagged with asymbol not expected to occur in normal text such as “_cap_” beforeconverting all text to lower case. Alternatively, all text may beconverted to upper case. The above example may then be further processedto read “_cap_jacob is not sick_comma_but_cap_jane is_period_” so thatcapitalization and punctuation errors are counted.

In some embodiments, all errors may receive equal weight, regardless ofimportance. When counting errors, then, a scorer may give each error aweight of one, and the error rate percentage may be the total number oferrors divided by the number of words. Additionally or alternatively,the scorer may use a perceptual accuracy metric that, for example,estimates the change in meaning or document similarity. For example, thescorer may assign different weights to various words or errors so thatthe total accuracy is a weighted sum of errors. Weights may be assigneddepending on factors such as severity, type, confusability, etc. Forexample, articles (e.g., “the”) may receive a medium weight (e.g., 0.5),capitalization and punctuation errors may receive a relatively smallweight (e.g., 0.1) and other words may receive a nominal weight (e.g.,1.0). Weights may be assigned based on any of a number of criteria orcategories including:

-   -   1. Weights may be assigned for each word based on a table entry.        For example, a table may specify that the word “not” has a        weight of 1.2, “ibuprofen” has a weight of 1.5, “so” has a        weight of 0.4, etc. A default value for words not in the table        may be set, for example, at 1.0.    -   2. Weights may depend on the word type. For example,        conjunctions (“and”) may have a relatively low weight (0.1),        proper nouns (“David”) may have a high weight (1.5),        capitalization may have a medium weight (0.5), punctuation may        have a medium-low weight (0.25), and, where otherwise not        specified, words may have a nominal weight (1.0).    -   3. Weights may be computed using a formula derived from data.        -   a. For example, weights may be selected so that frequent            words receive a lower weight than rare words. For example,            weights may be based on the word entropy, which may be            proportional to −p(word)*log(p(word)), where p(word) is an            estimated probability of the word appearing in a            transcription.        -   b. In some embodiments, weights may be based on the            conditional probability of a word in context (e.g.,            neighboring words), which may be determined using a language            model. For example, weights may be proportional to −p(word            context)*log(p(word context)), where context may be one or            more neighboring words and p(word context) is determined            using a language model.    -   4. Weight may be responsive to the length of the word.    -   5. Weight may be responsive to the importance of the word in        context. For example, in “administer a 2.5 mg dose of        methotrexate once per week,” the words “2.5,” “mg,”        “methotrexate,” and “week” are more important than “of” and        “per.” The “2.5” may also be more important in this context than        in “I thought about it for 2.5 seconds.” The weight in context        may be computed by labeling each word in a training set with an        importance score. A machine learning method such as logistic        regression, neural network training, or another method in Table        9 may be used to learn, for example, from a labeled training        set, how important a given word may be in a given context.    -   6. Weight may be a measure of the impact a word error has on        meaning for a phrase, sentence, or other string of words. For        example, the phrase “I'm now ready” misrecognized as “I'm not        ready” may have a greater impact than “I'm now ready”        misrecognized as “I'm all ready.”    -   7. Weight may be related to the similarity in meaning of a word        to the misrecognized word. Synonyms may have low weights,        unrelated words may have high weights. For example, “the”        misrecognized as “that” may have a low weight, whereas “dancer”        misrecognized as “dagger” may have a high weight. Similarity may        be measured, for example, using an ontology or by measuring        distance between vector forms of the words, such as word        embeddings. Methods for measuring word similarity include latent        semantic analysis and comparing word embeddings, which may be        determined using Word2vec.    -   8. Weight may be responsive to a position in the communication        session. For example, errors during the first 10 seconds of the        communication session may receive a relatively higher weight.    -   9. Weight may be responsive to the distance between a word's        position in the hypotheses and its correct location. For        example, suppose a hypothesis contains the correct words, but        one word is in the wrong place. In some embodiments, the        hypothesis may be given partial credit for recognizing the word,        even though the word is incorrectly positioned. The weight of        the error may, for example, be proportional to the number of        words between the hypothesized location of the misplaced word        and its correction position.

In some embodiments, the equivalency table 5730 may be enhanced byincluding context, such as neighboring words or other symbols. Forexample, in some embodiments, if the audio includes “I see Dr. Krishtomorrow,” the equivalency table may equate “Dr.” to “{doctor/drive},”so that if the recognizer hears “I see drive Krish tomorrow,” it may becounted as correct. In some embodiments, this type of error may bedetected by using an equivalency table containing entries that specifythe context in which each alternative (e.g., “doctor” or “drive”) isallowed. For example, if “Dr.” is preceded by a capitalized word or aword likely to be a proper noun (e.g., “Smith Dr.”), then only “drive”may be allowed, but if “Dr.” is followed by a capitalized word or a wordlikely to be a proper noun (e.g., “Dr. Adams), then only “doctor” may beallowed.

In some embodiments, other perceptual accuracy metrics such as thoseused to measure language translation quality may be used. Examplesinclude the Bleu score, which measures the correspondence between amachine's output and that of a human, and METEOR (Metric for Evaluationof Translation with Explicit Ordering). Modifications, additions, oromissions may be made to the environment 5700 without departing from thescope of the present disclosure.

FIG. 53 illustrates an example environment 5800 for denormalizationmachine learning, in accordance with some embodiments of the presentdisclosure. In some embodiments, machine learning, such as naturallanguage processing (NLP) training, may be taught a method to performdenormalization of a transcription as illustrated in environment 5800.In these and other embodiments, a model trainer 5802 may be configuredto learn from a set of feature values (the features input) and targetvalues (target input) and train a translation model for converting anormalized string to a denormalized string. The model trainer 5802 mayuse normalized and denormalized text during the training from a trainingset 5810 of text.

In some embodiments, the normalized text and the denormalized text maybe obtained from an ASR system 5820 that includes a word recognizer 5804and a normalizer 5806 a. The ASR system 5820 may be provided audio. Theword recognizer 5804 may generate denormalized text and provide thedenormalized text to the training set 5810 and to the normalizer 5806 a.The normalizer 5806 a may be configured to normalize the text andprovide the normalized text to the training set 5810. In someembodiments, the training set 5810 may be stored. In some embodiments,the audio may be recorded audio or live audio of a communicationsession, among other types of audio. Alternatively or additionally, theASR system 5820 may not provide the denormalized text. In these andother embodiments, a separate system, such as one that receives inputfrom humans, may generate the denormalized text from the normalized textoutput from the ASR system 5820. Additionally or alternatively, othersources of the normalized and denormalized text may be used, includingtext created by a human translator or a rule-driven machine translator.For example, a machine translator may be given denormalization rulessuch as to split compound words, to spell out abbreviations, expandcontractions, and convert digit strings to spelled digits, and to use apre-defined spell checker that allows only one spelling of each word.Text created by the machine translator may be used as a denormalizedtext in the training set 5810.

The training set 5810 may be provided to feature extractors 5840. Thefeature extractors 5840 may be configured to determine features such asn-grams or word embeddings of the training set 5810 that may be providedto the model trainer 5802. Alternatively or additionally, the featureextractors 5840 may be omitted in embodiments where raw data may be usedas an input to the model trainer 5802. The model trainer 5802 maygenerate a denormalization model which may be structured and trainedusing methods designed for language translation or methods todenormalize text for text-to-speech synthesis. The denormalization modelmay be provided to the denormalizer 5830. Other machine learningmechanisms that may be used to train models for the denormalizer 5830include methods listed in Table 9.

An example of the operation of the denormalizer 5830 is now provided.Audio may be transcribed by a transcription unit 5814 that may include anormalizer 5806 b. The normalized transcription may be denormalized bythe denormalizer 5830 using the model built by the model trainer 5802from the training set 5810. The denormalizer 5830 may convert normalizedtext strings into denormalized strings. For example, a caller may recitean address by saying “One twenty three Lake Shore Drive, Gary, Ind.” Thetranscription unit 5814 may transcribe the audio as, “123 Lake ShoreDr., Gary, Ind.” The denormalizer 5830 may output “one twenty three lakeshore drive gary indiana” (in this example ignoring capitalization andpunctuation) as the denormalized string.

Modifications, additions, or omissions may be made to the environment5800 without departing from the scope of the present disclosure. Forexample, the denormalizer 5830, in some embodiments, may include afinite state transducer. Other methods for language translation,preprocessing text for text-to-speech synthesis, or implementinglanguage processing functions may also be used by the denormalizer 5830.Alternatively or additionally, the environment 5800 may be used to trainother models. For example, using features as a first input to the modeltrainer 5802 and target values as a second input, the environment 5800may be used to train models for capitalization, punctuation, accuracyestimation, or transcription unit selection.

FIG. 54 illustrates an environment 5900 for denormalizing text, inaccordance with some embodiments of the present disclosure. Asillustrated, a transcription unit 5914 may transcribe audio into anormalized string using a first ASR system 5920 a and a normalizer 5906.The same audio that the transcription unit 5914 transcribes may also besent to a second ASR system 5920 b. In some embodiments, an expander5902 may obtain the normalized string from the transcription unit 5914and may be configured to create a structure, such as a lattice, grammar,word graph, or n-best list that describes various ways in which the textmay be pronounced. For example, “123” may be uttered as “one twentythree,” “one two three,” or “one hundred twenty three.” If a lattice isused, each possible path through the structure may trace a variation inhow the normalized string may be denormalized. If an n-best list isused, each candidate on the list may represent a denormalization optionfor a phrase. The expander 5902 may be rule-based or it may use adata-driven transducer or translation-based method. A rule-basedexpander may use, for example, a series of regular expressions or anextended version of regular expressions to map input strings to multipleways in which they might be spoken.

In some embodiments, the structure created by the expander 5902 may beconverted to a grammar and provided to the second ASR system 5920 b. Thesecond ASR system 5920 b may attempt to transcribe the audio into one ofthe alternatives defined by the structure. The string recognized by thesecond ASR system 5920 b, such as the most likely path through thelattice or the most likely candidate from the n-best list, based onacoustic evidence from the communication session audio, may be used asthe denormalized string. The environment 5900 may also be used togenerate normalized and denormalized text data for training a machinelearning denormalizer such as the embodiment described above withreference to FIG. 53. Modifications, additions, or omissions may be madeto the environment 5900 without departing from the scope of the presentdisclosure.

FIG. 55 illustrates an example fuser 5424, in accordance with someembodiments of the present disclosure. In some embodiments, the fuser5424 may obtain transcriptions from each of multiple transcription units5414 a, 5414 b, and 5414 c, collectively the transcription units 5414.In some embodiments, the transcription units 5414 may include revoicingand non-revoicing transcription units. Alternatively or additionally,one or more of the transcription units 5414 may include ASR systems thatare crippled.

In some embodiments, the audio inputs to the transcription units 5414may be substantially identical, being derived from a common source suchas audio from a communication session. Alternatively, audio inputs maybe derived from multiple sources. For example, a first audio input maybe derived from a voice sample spoken by a caller such as atranscription party and a second audio input may be revoicing of thefirst audio input. Additionally or alternatively, a first audio inputmay be derived from a voice sample spoken by a first caller and a secondaudio input may be derived from a voice sample spoken by a secondcaller. The transcriptions may be provided to the fuser 5424.

The fuser 5424 may obtain the transcriptions, for multiple purposes,including:

-   -   1. Generating a fused transcription.    -   2. Using transcription unit 5414 output, including confidence        scores from each transcription unit 5414 and agreement between        the transcriptions, to create a quality estimate of the fused        transcription.    -   3. Aligning, at an aligner 5404, the multiple transcriptions and        using the alignment for fusion and for estimating quality by the        quality estimator 5402.    -   4. Using a quality estimate for at least a segment of one of the        transcriptions to affect the outcome of fusion voting at a voter        5406.    -   5. Estimating the quality, by the quality estimator 5402, of one        or more of the transcription units 5414 for at least a portion        of the communication session or across multiple communication        sessions.    -   6. Making a selection among transcription units.

The transcriptions from the transcription unit 5414 may be provided todenormalizers 5420 that may denormalize the transcriptions and providethe denormalized transcriptions to an aligner 5404. The denormalizedtranscription may be aligned by the aligner 5404. The alignedtranscriptions may be provided to the voter 5406. The voter 5406 maycompare the aligned transcriptions to determine one or more agreement(or disagreement) rates between the aligned transcriptions.

In some embodiments, the transcription units 5414 may also provideinformation regarding the transcription, including a confidence score ofthe transcription. In these and other embodiments, as a new segment ofeach transcription is generated by each transcription unit 5414, thetranscription units 5414 may create additional information such as aconfidence score for a segment of the transcription which may include atleast part of the new segment.

In some embodiments, the quality estimator 5402 may use information fromthe transcription units 5414, the aligner 5404, and/or the denormalizers5420 to estimate a quality of at least one segment of one of more of thetranscriptions. In some embodiments, the information may includeconfidence scores, transcription agreement rates, and other features(see Table 2 and Table 5). The quality estimate may be used to guide thefuser 5424, which may include guiding the aligner 5404 and/or the voter5406. An example operation of the fuser 5424 may include the following:

-   -   1. One or more transcription units 5414 receive an audio sample        and begin transcribing to create transcriptions.    -   2. Segments of the transcriptions from multiple transcription        units 5414 may be aligned by the aligner 5404 in real time.    -   3. The transcription segments may be compared, by the voter        5406, pairwise to determine agreement or disagreement rates.        Words may be scored as correct, substitution, insertion, and        deletion. Alternatively, words may be scored as correct or        incorrect.    -   4. The quality estimator 5402 may be used to estimate accuracy        or another quality estimate of one or more transcription        segments. The quality estimator 5402 may be configured to        utilize a method, such as a method from Table 9, trained on        transcriptions from the transcription units 5414 on        communication sessions for which the true accuracy is known and        is used as a training target for the quality estimator 5402. The        quality estimator 5402 may use features, such as agreement or        disagreement rates, extracted by the aligner 5404 to process ASR        confidence measures, and other features such as those in Table 2        and Table 5 to estimate the quality of one or more transcription        segments.    -   5. The aligned transcription segments may be used as inputs to        the voter 5406.    -   6. The transcription quality estimates may be used by the fuser        5424 for voting. For example, the quality estimates may be used        to bias the voting or break ties in favor of transcription        segments with higher-quality estimates.    -   7. The quality estimator 5402 may determine an estimated average        quality estimate for the communication session. The average        quality estimate may be based at least partly on the        segment-based quality estimates. The estimates or average        estimate may be used, for example, for CA feedback, input to a        CA activity monitor, advisements to CA supervision, alerts, and        reports.    -   8. The segment-based and/or average quality estimates may be        used as input for selection of transcription units.

Modifications, additions, or omissions may be made to the environment5500 without departing from the scope of the present disclosure.

FIGS. 56-83, among others, describe various systems and methods that maybe used to generate models, such as a language model or an acousticmodel, that may be used in ASR systems. Generating models may includetraining the models. In these and other embodiments, the models may betrained using transcriptions and audio of communication sessions withoutstoring the transcriptions and the audio past or substantially past thetermination of the communication session. The selection of thetranscriptions to use for training of the models may be based onstatistics of the transcriptions that may be generated as described withrespect to FIGS. 44-55.

FIG. 56 illustrates an example environment 6000 for training an ASRsystem, in accordance with some embodiments of the present disclosure.The environment 6000, in some embodiments, may be configured to train anASR system by training or adapting models that may be used by the ASRsystem. In some embodiments, a user device 6010 extracts and sendscommunication session audio to a transcription unit 6014. Thetranscription unit 6014 may generate transcriptions based on thecommunication session audio using an ASR system and return thetranscriptions to the user device 6010. Alternatively or additionally,the audio may be any type of audio that may be received by thetranscription unit 6014.

In some embodiments, the transcription unit 6014 may capturecommunication session data during the process of generating thetranscriptions and providing the transcriptions to the user device 6010.In these and other embodiments, the transcription unit 6014 may beconfigured to store the communication session data in a database 6002.Examples of communication session data may include those listed below inTable 15.

TABLE 15 1. Audio from one or more calling parties. 2. Text such ascommunication session transcriptions and the transcription source (e.g.,CA employee number). 3. Log data. 4. Time. 5. Phone numbers or deviceidentifiers. 6. Phone types. 7. Vocabulary words. 8. Account types. 9.Word embeddings. 10. Features derived from audio. 11. Action taken by acaptioning service such as an ASR/C A selection. 12. Disassociatedand/or de-sequenced segments of text or audio. 13. Models. 14. Modelparameter weights. 15. A voiceprint. 16. Data that may be used to createa voiceprint. 17. Results of analysis of communication session data. 18.Statistics derived from communication session data such as n-grams orn-gram counts. 19. Demographic and other information about the callingparties.

In some embodiments, a privacy filter 6004 may remove sensitiveinformation from the communication session data before the communicationsession data is stored in the database 6002. In these and otherembodiments, a model trainer 6006 may access the stored communicationsession data and use the stored communication session data to train newmodels for ASR systems. For example, the new models may be used by oneor more ASR systems in the transcription unit 6014.

Additionally or alternatively, the transcription unit 6014 may send thecommunication session data to the model trainer 6006. The model trainer6006 may use the communication session data to train one or more modelson-the-fly. In these and other embodiments, training on-the-fly mayinclude not storing the communication session data, other than during abrief interval, such as during the communication session. For example,in some embodiments, training on-the-fly may include the communicationsession data being deleted at the end or within 1, 5, 10, 15, 20, 30, or60 seconds of the end of a communication session from which thecommunication session data is obtained. Alternatively or additionally,training on-the-fly may include only storing the communication sessiondata in volatile memory and not in a static, non-volatile, or long-termmemory storage such as a long term database. In these and otherembodiments, the updates to the models, including weight adjustments,counts such as n-gram counts, and other model parameter changes, may beretained, but the communication session data may be deleted.

In some embodiments, the model trainer 6006 may be configured to trainvarious models from varying types of communication session data. Thus,based on the type of communication session data obtained, the modeltrainer 6006 may generate particular types of models. Examples of themodels that may be built by the model trainer 6006 and data types thatmay be used to train the models may include:

-   -   1. Acoustic Models (AMs) may be trained from audio, which may        include recordings from subjects recruited for recording, CAs,        actors, and callers (such as various speakers on the first and        second devices). AM training may alternatively use features in        place of audio, where the features are extracted from audio, to        train models. AM training may also use text from transcriptions        that correspond to content of the audio. Text may be obtained        from transcriptions generated for use in the production service        or from transcriptions created by transcribers. Transcribers may        be machines and/or humans.    -   2. Language Models (LMs) may be trained from text such as        transcriptions. LMs may alternatively be trained from n-grams or        synthesized n-grams such as n-grams generated from an RNNLM.    -   3. Confidence models may be used to estimate transcription unit        confidence, accuracy, quality, or probability. Confidence models        may be trained from reference transcriptions, hypothesis        transcriptions, audio, features, log data, and other information        generated as communication sessions are processed in a        production or test system. Confidence models may also be built        using information regarding the extent to which the audio was        transcribed correctly, transcription accuracy, or transcription        confidence. Confidence models may be built using reference        transcriptions (such as transcriptions generated by offline        transcriptionists, transcriptions generated from a production        service, transcriptions created from audio using ASR) and        hypothesis transcriptions (such as transcription unit        transcriptions). Confidence models may use input from features        listed in Table 5.    -   4. Classification models (a.k.a. selection models or        transcription unit selection models) may be trained using inputs        such as those that may be used to train confidence models, plus        features from Table 2. Classification models may be used to        select a transcription method from among one or more methods        described in this disclosure. (see Table 1).    -   5. Punctuation models may be trained on data where punctuation        has been added. CAs, for example, may revoice keywords to add        punctuation and thus generate training data for an automatic        punctuator.    -   6. Capitalization models may be trained on data where letters        are correctly capitalized. CAs, for example, may revoice        keywords to add capitalization or type transcriptions and thus        generate training data for an automatic capitalizer.    -   7. Summarization models may be trained on communication session        transcriptions.

Modifications, additions, or omissions may be made to the environment6000 without departing from the scope of the present disclosure. Forexample, in some embodiments, part or all of the model trainer 6006 maybe run in one or more locations, including those described above withreference to FIG. 1 or other figures in this disclosure.

FIG. 57 illustrates an example environment 6100 that uses data to trainmodels, in accordance with some embodiments of the present disclosure.In some embodiments, the environment 6100 may be configured to trainmodels such as acoustic models and language models. The environment 6100may include a transcription unit 6114 that is configured to transcribeaudio to generate one or more transcriptions. The transcription unit6114 may include ASR systems 6120 a-c, collectively the ASR systems6120, an audio interface 6118 to obtain revoiced audio and provide therevoiced audio to the ASR systems 6120 a and 6120 b, a text editor 6126for editing a transcription output by the ASR system 6120 a, and a fuser6124 for fusing the transcriptions output by the ASR systems 6120.

In some embodiments, the environment 6100 may include a database 6102.The database 6102 may be configured to store data such as transcriptionsfrom the transcription unit 6114 including other data as described withrespect to Table 15. Examples of the data that may be stored by thedatabase 6102 may include:

-   -   1. Audio, which may include audio samples from one or more        speakers, including the subscriber, the transcription party        speakers, and other speakers on a communication session.    -   2. Revoiced audio, which may be audio sampled from one or more        CA voices. Revoiced audio may be captured from CAs during        revoicing of communication sessions.    -   3. Transcriptions and confidence or accuracy scores from one or        more ASR systems such as ASR0 6120 a (which may be        speaker-dependent), ASR1 6120 b, and ASR2 6120 c, the text        editor 6126, and the fuser 6124. In some embodiments, the ASR1        6120 b, and ASR2 6120 c may each include multiple ASR systems.    -   4. Data (such as n-grams, speech features, and new or adapted        models) extracted from audio, transcriptions, or other        communication session data.    -   5. Data from external sources, including other voice services,        data collections, purchased and publicly available data, and        data scraped from websites.

The database 6102 may provide long-term storage where the data is savedand processed. Alternatively or additionally, the database 6102 may be ashort-term buffer where a portion of the data may be deleted after aspecified event has occurred, such as the end of a communicationsession, a particular amount of time, such as before the end of thecommunication session, a particular amount of time after the end of acommunication session, at a time where transcription has been completedor delivered to a user device, or as soon as the data has been used fortraining.

In some embodiments, a subset of the data may be processed by an onlineor off-line transcriber 6104 to transcribe audio into text, correcterrors in existing transcriptions, or annotate additional informationsuch as gender, demographic, age (child, elderly, etc.), speech orhearing impairment, accent, parts of speech, named entities, newspeaker, punctuation, capitalization, sentence and phrase boundaries,speaker intent, content summaries, speaker sentiment or emotional state,audio quality, and topic, among others. Additionally or alternatively,audio and/or transcriptions may be annotated to tag the transcriptionwith information such as the additional information listed above usingan automated labeler 6106. Such annotations may include speechrecognition, gender detection, punctuation and capitalization, naturallanguage processing, summarization, topic analysis, and sentimentanalysis, among others. In some embodiments, the automated labeler 6106may be implemented as part of an ASR system that returns a transcriptionor other form of text result that includes the annotations, such as XMLfiles, JSON files, WCN, lattice, or an n-best list. For example, one ormore of the ASR systems 6120 may include the automated labeler 6106.

In some embodiments, the automated labeler 6106 may be configured togenerate transcriptions of recorded audio. Additionally oralternatively, the automated labeler 6106 may be configured to generatetranscriptions of audio as the audio is received and processed as partof providing a service and training models. After training the models,the audio and/or transcriptions may be deleted. In these and otherembodiments, the automated labeler 6106 may generate the transcriptionsusing one or more human transcribers, one or more ASR systems, or acombination thereof, including the various configurations described withrespect to transcription units disclosed in this disclosure.

In some embodiments, the environment 6100 may include an ASR modeltrainer 6108, which uses the data from the database 6102 to train newmodels such as language models by an LM trainer 6119. In someembodiments, to train language models, the data may include atranscription. The ASR model trainer 6108 may also include an AM trainer6117 that may be configured to generate acoustic models using the datafrom the database 6102. In some embodiments, to train the acousticmodels, the data may include audio and transcriptions.

In some embodiments, the ASR model trainer 6108 may incorporateconfidence scores provided by the ASR systems 6120 in training thelanguage and acoustic models. For example, the ASR model trainer 6108may weigh training data samples from sample transcriptions according tothe estimated accuracy or the confidence of accuracy of the sampletranscriptions. Additionally or alternatively, the ASR model trainer6108 may factor in CA performance such as CA accuracy during testinginto the model training process. For example, the ASR model trainer 6108may give greater weight to transcriptions from CAs with higherhistorical performance or may train models using data from CAs scoringabove a selected threshold. After training the new models, the ASR modeltrainer 6108 may provide the new models to the ASR systems 6120. The ASRsystems 6120 may use the new models to transcribe the current or futurecommunication session audio. Modifications, additions, or omissions maybe made to the environment 6100 without departing from the scope of thepresent disclosure.

FIG. 58 illustrates an example environment 6200 for training models, inaccordance with some embodiments of the present disclosure. In someembodiments, the environment 6200 may be configured for training modelscontingent on consent from participants of a communication session. Theenvironment 6200 may include a transcription unit 6240 that may beconfigured with a diarizer 6201, a CA client 6250 associated with a CAthat may revoice audio of the communication session. The diarizer 6201,in some embodiments, may be configured to identify various voices in theaudio of the communication session. In some embodiments, the audio maybe directed to the diarizer 6201 and the CA client 6250. The diarizer6201 may identify the different voices and send the audio associatedwith the different voices to a consent detector 6202. The CA client 6250may provide revoiced audio from the CA to the consent detector 6202.

The consent detector 6202 may determine whether the CA and the peopleassociated with the voices in the audio of the communication sessionhave provided consent to record, transcribe, extract statistics such asn-grams, use for ASR or model training, provide captions, or otherwiseuse the voices of the people and the CA.

In some embodiments, a consent database 6204 contains consent policies.Consent policies may define rules and methods and may be changed basedon shifts in company guidelines, procedures, court rulings, requirementsfrom regulatory agencies, customer/vendor contracts, and legal statutes.The policies may differ based on what data is being captured from thecommunication session. Examples of the data that may be captured isprovided in Table 15.

In some embodiments, each party, such as each voice in the communicationsession and the CA, and with respect to a communication session, mayhave an associated set of consent records and rules. The consentdetector 6202 may be configured to determine, for each piece of data,whether the environment 6200 has adequate consent to use the data forpurposes such as to (1) provide a transcription service, (2) train oradapt models for ASR, confidence, capitalization, punctuation, etc., (3)extract statistics such as n-grams, (4) record communication sessionaudio, (5) record communication session transcriptions, (6) record othercommunication session data. The consent detector 6202 may also determinewhether only non-private data may be used or whether private data mayalso be used.

In some embodiments, the consent database 6204 may include a record ofthe type of consent obtained from each party. Types of consent may varydepending on what is being recorded. Consent records may include or maybe derived from signed agreements, activity on a website, interactionwith a user device, etc. Consent records may include information on thetype of data that may be captured, how the data may be used, whether theconsent applies to a minor and/or is provided by a parent or guardian,the identity of the entity providing consent (e.g. the party, aguardian, an authorized representative, a court issuing a warrant orsubpoena, a federal law, a state law, a local ordinance, a notice orregulation from a regulatory agency, a court ruling, a legal opinionsuch as from a law firm, or a government agency providing a waiver orother legal authorization) and the relationship of the party to theentity providing consent, and notations on revoked consent. In someembodiments, consent may be requested in exchange for providing atranscription service, a promise to use the training to improveaccuracy, for a discount (including free) on a transcription or otherservice, or for monetary compensation.

In some embodiments, the environment 6200 or person collecting consentmay advise the consenting party on procedures for revoking consent ordeleting stored data. A prompt, such as text on a display or an audiorecording, may advise one or more parties as to the process for revokingconsent. A party may revoke consent using mechanisms similar to thosefor providing consent such as via a website, soft key, voice command, orDual Tone—Multi Frequency (DTMF) input. If a party revokes consent,communication session data may be deleted and further recording andgathering of data may be discontinued. A confirmation prompt may beplayed to one of more of the parties such as “This communication sessionwill not be recorded.” For example, a DTMF detector may be configured todetect a DTMF sequence such as “##” during a communication session and,if detected, may delete the communication session data and discontinuefurther storing of data.

If consent is granted or refused, the party's response may be saved inthe consent database 6204 and retrieved during future conversations. Insome embodiments, during future conversations, the prior consent recordmay be retrieved from the consent database 6204 by the consent detector6202, and, if deemed to remain in force, the consent detector 6202 mayindicate that consent is granted. In these and other embodiments, theconsent detector 6202 may be configured to present a recordedannouncement regarding the previously-obtained consent to indicatecommunication session data may be captured during the currentcommunication session, such as recording of audio, before capturing ofthe communication session data. Alternatively or additionally, theconsent detector 6202 may be configured to require a response from theconsenting party before capturing communication session data for everycommunication session. Consent may be collected through one or more ofseveral mechanisms:

-   -   1. Consent may be collected as part of a CA's employment        agreement.    -   2. A human representative or automated system may bridge onto a        communication session such as a captioned communication session,        place a communication session, or receive a communication        session and ask one or more parties for consent. The human        representative or automated system may use, for example, text or        audio to make the request and DTMF or speech recognition to        collect the response. The communication session may be audio or        video.    -   3. An automated recording may be played for one or more parties        on a communication session advising them that the communication        session may be recorded or otherwise used.    -   4. A service may send an SMS, MMS, IM, chat, email, or other        text message to a party asking for consent. Consent may be        collected by a return text message, by selecting an option        presented by the text message, or following a link, such as a        link provided in the text message.    -   5. A user device may advise the user of the data collection        and/or ask for consent, such as by playing a recording or        displaying text on the screen. The user device may collect        consent via a screen tap, button press, vocal authorization,        touch tones, a mouse click, or by following a link.    -   6. An application running on a PC, smartphone, computer, tablet        or other user device may advise a party that the communication        session may be intercepted and may request consent via a        recorded or synthesized audio prompt, displayed text, or other        mechanisms. The party being asked for consent may grant or        refuse consent verbally (to be understood by a person or ASR        system), by gestures such as screen swipes or clicks, via sign        language, using keyboard and/or mouse input, by use of other        input devices, by following a link to a site providing details        and collecting consent, or by continuing to use the service with        the understanding that doing so implies consent.    -   7. A party may grant consent by signing a service agreement (on        paper or electronically) or by otherwise agreeing to conditions        of service such as a EULA.    -   8. A party may sign a consent form.    -   9. A party may grant consent on a website.    -   10. A first party on the phone conversation such as a subscriber        (Party 1) may ask a second party (e.g., the transcription party)        for consent. A service representative or an ASR system may        listen to the request and to the second party's answer, then        record the response as audio and/or text. Alternatively, the        first party may take action such as pressing a button to        indicate consent on behalf of one or both parties.    -   11. An analyzer may evaluate the legality of recording based on        consent provided by one or more calling parties and on state        laws pertaining to one or more calling parties. For example, if        a first party in a one-party state provides consent, the        analyzer may determine whether a second party is also in a        one-party state, and if so, may determine whether the second        party may be recorded.    -   12. A first device used by a first party (e.g., Party 1) may        transmit a message to a second device used by a second party        (e.g., the transcription party) requesting consent. The second        device may request consent from the second party, and may        provide links or text providing details of the terms and        policies related to consent. The user of the second device may        indicate consent using the second device and his/her response        may be transmitted to the first party's device and/or stored in        the consent database.    -   13. Playing periodic beep tones indicating that the        communication session is being recorded.    -   14. A first party may be offered an incentive for providing        consent. An application may display on a screen an offer to        provide captioning, communication session transcriptions,        conversation summarization, the ability to search and query        content of current and previous communication sessions, or other        features and benefits in return for consent. The display may        also invite the first party to ask a second party for consent.    -   15. The party being asked for consent may receive a link or        other option for viewing terms and conditions, privacy policies,        and other details regarding consent.    -   16. A parent or legal guardian may provide consent for a minor        or child under the legal age of consent.    -   17. A party with power of attorney may provide consent for        another party, such as a party who is not competent (e.g. lacks        mental capacity) to provide consent.    -   18. A government entity or an authorized representative may        provide consent on behalf of the party to which the consent        applies.

In some embodiments, in response to the consent detector 6202determining that a party has consented to the use of the communicationsession data, the consent detector 6202 may direct the party'scommunication session data to a model trainer 6230 or a database 6222for storage. In response to the consent detector 6202 determining thatthe consent is inadequate, the consent detector 6202 may take actionduring or after the communication session such as playing or displayingprompts to a party (e.g., “Click ‘OK’ to allow us to record thecommunication session”) and collecting further consent information fromthe party. In some embodiments, the consent detector 6202 may determinethat adequate consent exists for a first party (e.g., Party 1), but notfor a second party (e.g., the transcription party) and enable trainingand/or recording for the first party only.

In some embodiments, the consent detector 6202 may take into account thelocality of the calling parties in making the decision to train and/orrecord (see #11 on the list above). Suppose, for example, a firstparticipant of a communication session grants consent and is in aone-party state (a state that allows recording of a communicationsession as long as one party consents). The consent detector 6202 mayenable training and/or recording for the first participant. Depending onan assessment of the legality, consent detector 6202 may enable trainingand/or recording for a second participant of the communication session.For example, in response to the first and second participants being inone-party states or locales, the consent detector 6202 may enabletraining and/or recording for both parties. In these and otherembodiments, the consent detector 6202 may determine the locations ofthe participants based on phone numbers or other device identifiers ofthe participants associated with one or more one-party states. In someembodiments, the consent detector 6202 may further consider otherevidence regarding the locations of the participants such as currentlocation estimated from GPS, IP address, or proximity to cell towers inknown locations.

In some embodiments, if a first participant is in a one-party state anda second participant is in a two-party state (a state that requiresconsent from both or all parties) and only the first participant hasgranted consent, then the consent detector 6202 may collect varyingamounts of communication session data for each participant. For example,the consent detector 6202 may enable collecting n-grams for the secondparticipant and may enable recording of audio for the first participant.Alternatively or additionally, if either participant is in a two-partystate or if the state is unknown, the consent detector 6202 may decideto collect data based on factors other than locality, such as currentfederal laws and regulations, or may request consent from one or moreparticipants.

In some embodiments, the consent detector 6202 may detect consent fromone or more participants of a communication session and treatcommunication session data from each participant according to theconsent status and applicable policies for that participant. Forexample, a CA, a subscriber, a first speaker using a transcriptionparticipant device, and a second speaker using a transcriptionparticipant device may each have different consent status and may eachfall under different policies. In this example, data collection bydatabase 6222 and model training by model trainer 6230 for each of theaforementioned participants may be governed by the decisions of theconsent detector 6202 for each respective participant.

As illustrated, the training components of the model trainer 6230 areseparate; however the training components may be combined into fewer ormore components, depending on the training and consent methodsimplemented. Similarly, the consent detector 6202 may act for multipleparties; however it may be divided into multiple consent detectors 6202,each for one or more parties. The training components may train, create,or adapt separate models for individuals or groups of individuals, orthey may train, create, or adapt speaker-independent models on data frommultiple callers. For example:

-   -   1. Collections of audio samples may be combined, weighted        according to how much influence each data source should have in        the final result, and used to train an acoustic model to be used        for any of multiple callers. In some embodiments, data        collection and ASR model training may be responsive to accuracy        or confidence scores, such as by weighting training data samples        according to estimated accuracy or confidence of the sample ASR        result. Estimated accuracy or confidence may be responsive to        whether the sample was transcribed by a revoiced or non-revoiced        ASR system, or by a combination thereof (see Table 1). In        another example, ASR model training may train only on samples        where confidence is above a selected threshold.    -   2. CA speech may be used to train ASR models adapted to        recognize CAs. An ASR system using this CA-trained model may be        used along with or instead of a speaker-dependent ASR.    -   3. Participant speech may be used to train or adapt a language        model for transcribing the participant's side of the        conversation.    -   4. Multiple data sources may be combined to train a single model        or set of models. Additionally or alternatively, separate models        may be built for each voice or group of voices.

In some embodiments, multiple types of models may be trained using thecommunication session data. Models that may be trained may includeacoustic models, or language models, among others. In these and otherembodiments, models may be trained for specific parties, such as a CAand/or participants of the communication session. In these and otherembodiments, the model trainer 6230 may include trainers for eachspecific party. For example, the model trainer 6230 may include a CAacoustic model trainer, Party 1 acoustic model trainer, the Party 2acoustic model trainer, etc. The model trainer 6230 may be responsive tothe consent status or output of the consent detector 6202 and may, forexample, select a model to be trained or the manner of training basedon, for example, the existence of or type of consent. In someembodiments, the ASR model trainer 6230 may include the following typeof training models:

-   -   1. A CA trainer 6210 may be configured to train models adapted        to multiple CA voices from CA audio and transcriptions collected        across multiple CAs. The transcriptions may be derived from an        ASR system listening to the CA voice, from a text editor, from a        fuser, from an offline transcription, etc. These models may be        used by ASR systems for multiple CAs, where the ASR systems may        be used in combination with or in place of a CA-adapted ASR        system that may be speaker-dependent.    -   2. A Party 1 trainer 6212 may be configured to train models        adapted to Party 1's voice. These models may be used to        transcribe Party 1's voice from a Party 1 device. Transcriptions        of Party 1 may be provided to the Party 1 device and/or the        device of the transcription party. In these and other        embodiments, the Party 1 may be a subscriber of a transcription        service that includes the environment 6200.    -   3. A Party 2a trainer 6214 may be configured to train a first        set of transcription party models adapted to a first speaker        using a transcription party device that may be participating in        the communication session with the Party 1 device. Models may be        trained on text and/or audio collected when the diarizer 6201        determines that the first speaker is speaking. An ASR system may        use the first set of transcription party models to generate a        transcription when the diarizer 6201 determines that the first        speaker is speaking.    -   4. Party 2b trainer 6216 may be the same as the Party 2a        trainer, except that models are trained on and used for a second        speaker using the transcription party device and detected using        the diarizer 6201.    -   5. The model trainer 6230 may train one model for all        subscribers (hearing impaired parties) and another model for all        transcribed (nominally hearing) parties.

In some embodiments, a consolidator 6208, such as an acoustic modeltraining and consolidation tool, may be included in the model trainer6230. The consolidator 6208 may be configured to combine into a singlemodel, training results from the multiple trainers in the model trainer6230.

In some embodiments, content derived from participants, includingtranscriptions, models adapted to transcription parties, acousticmodels, language models, punctuation models, capitalization models,voiceprints, and data collected for training adapted models may bestored on a user device or on another device where access to the data iscontrolled by the user device under direction of a subscriber of thetranscription service that includes the environment 6200. Additionallyor alternatively, content derived from the subscriber may be stored on adevice where access to the subscriber data is controlled by thesubscriber and content derived from a transcription party may be storedon a device where access to the transcription party data is controlledby the transcription party. In some embodiments, data may be sent toanother location with permission from a party authorized to provideconsent or with access to the data.

In some embodiments, the transcription party audio transmitted to themodel trainer 6230 may be restricted in audio bandwidth (e.g., 4 kHz) orsampling rate (e.g., 8 kHz) due to having traversed a first network suchas a telephone network. In these and other embodiments, subscriber audiomay be captured at an audio bandwidth different than that of thetranscription party audio and sent to the model trainer 6230 by way of asecond network such as a data network. As a result, the ASR models andspeech recognition software used to transcribe subscriber audio may beconfigured for a bandwidth different than that of the transcriptionparty audio. For example, the ASR system and models used to transcribethe transcription party audio may use an 8 kHz sampling rate, while theASR system and models used to transcribe subscriber audio may use ahigher sampling rate such as 16 kHz.

An example of training models and storing data using the environment6200 may include the following operations:

-   -   1. Retrieve a device identifier such as caller ID from one or        more calling devices (e.g., the subscriber device or the        transcription party device).    -   2. Use the device identifier to index speaker-dependent ASR        models or diarization models 6218 or both. Diarization models        may include speaker voice models.    -   3. The diarizer 6201 retrieves the models thus indexed.    -   4. The diarizer 6201 listens to the audio stream to extract        speaker features.    -   5. The diarizer 6201 compares the speaker features to the        diarization models 6218 (a.k.a. voiceprints).    -   6. In response to the diarizer 6201 finding a match, meaning for        example that the difference between the speaker features and the        speaker voice model is within a selected threshold, then the        speaker's identity is determined. In some cases, multiple        parties may have similar voices, in which case the diarizer 6201        may group them together as a single voice for purposes of        training and identification.    -   7. The consent detector 6202 retrieves (1) the speaker's consent        record and (2) the current consent policy from the consent        database 6204.    -   8. In response to the identity determined for the speaker, the        speaker's consent status, and the consent policy, the consent        detector 6202 determines how communication session data from the        speaker may be used, for example to be stored, used for        training, and/or used to create or update a speaker voice model.    -   9. In response to the comparison failing to yield a match or if        there is only one known speaker corresponding to the speaker's        device identifier and if there is consent to create a speaker        voice model (or if consent is not needed), the model trainer        6230 may create a new speaker voice model for the speaker using        the data to train or adapt one or more models.    -   10. According to policies and the speaker's consent, the        database 6222 may store other communication session data from        the speaker. Stored data samples may be used, for example, for        measuring accuracy, training models, creating speaker voice        models, and as a basis for generating transcriptions.    -   11. Depending on criteria such as whether the match was        sufficiently close, and contingent on consent, the diarizer 6201        may update a speaker voice model and/or ASR model with features        extracted from the matched speaker.    -   12. The diarizer 6201 may send an updated model to the ASR        system.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations and actions, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

As an alternative to creating a model or models for each voice, themodel trainer 6230 may create a number of models that match multiplevoices. For example, (1) a population (say one-hundred thousand voices)may be clustered to identify, say, one-hundred groups of voices; (2)each voice may be assigned to the group or cluster that it most closelymatches (the clustering and assignment criteria may be a voiceprintmatch); and (3) when a voice is detected, the characteristics for thecluster assigned to that voice may be retrieved and applied to the voiceas if the cluster characteristics applied to that voice. Alternativelyor additionally, instead of using speaker voice models, the diarizer6201 may identify speakers based on a device identifier and may ignorethe sound of the speaker's voice. In these and other embodiments, allparties using the same device may be treated as one person for purposesof diarization, display, and training or adapting ASR models.Alternatively or additionally, the diarizer 6201 may identify speakersbased on estimated gender (using, for example, pitch). In these andother embodiments, all males using the same device may be treated as oneperson and all females using the same device may be treated as oneperson.

In some embodiments, the diarizer 6201 may detect whether the subscriberor the transcription party is speaking using one or more of multiplemethods, including: (1) receive a stereo signal containing audio fromthe two parties, one party per channel (if signals arecross-contaminated, the diarizer 6201 may use an echo canceller toeliminate the residual); or (2) in the case where both voices are on onechannel, match each voice against a voiceprint and identify the speakerbased on voiceprint match.

In some embodiments, the diarizer 6201 may maintain a record, during thecommunication session or across communication sessions, of how manypeople using a given device are speaking and a model that describes thesound of each persons voice. An example of a diarization model is avoiceprint, a pattern that matches a given speaker, based on thespeaker's voice. A voiceprint (i.e., a model used to confirm or detectthe identity of a speaker) may be used to identify when a speakerchanges and/or to identify the speaker. In some embodiments, identifyingthe speaker may indicate that a speaker's actual identity (e.g., name,location, company, account number, alias) is determined. Alternativelyor additionally, there may be multiple speakers on a communicationsession. In these and other embodiments, identifying the speaker mayinclude identifying the speaker as belonging to a particular voice amongthese multiple speakers and denote the speaker by an index or other codesuch as “speaker 3.”

In some embodiments, the diarizer 6201 may identify speakers by genderor other demographic, using, for example, age and gender detectionsoftware to distinguish males from females, children from adults, etc.,and may show the demographic information on a user device, such as auser device of a subscriber. An example of how gender may be displayedis:

Female speaker: I need to leave shortly.

Male speaker: I have to go too.

Child: Can I come?

If a speaker's name is known, the name may be displayed withtranscriptions, for example:

David: It's time to make a decision.

John: I agree.

In some embodiments, the diarizer 6201 may obtain the speaker's namefrom a reverse directory lookup, indexed by the speaker's phone numberor communication device identifier; from a name provided by thespeaker's communication device, from a contact list belonging to one ormore of the parties, from face identification and/or lip motiondetection on a video communication session, or from a voiceprintanalyzer working with an ASR system listening to the conversation anddetermining names from instances where parties verbally mention theirown name or names of others on the communication session.

In some embodiments, the diarizer 6201 may distinguish between multiplevoices from the transcription party's device. In these and otherembodiments, the diarizer 6201 may be used as part of the process oftraining separate speaker voice models and acoustic, language, and othermodels for these voices. The diarizer 6201 may distinguish these voicesfrom each other or identify them by matching them to separate speakervoice models, which may be part of the diarization models 6218.

In the depicted embodiment, an example additional voice may beidentified as party 2b as opposed to the first voice and the party 2avoice. Audio and/or text from this additional voice may be shown in thefigure as party 2b data and may be used to train a separate acousticmodel.

In general, the diarizer 6201 being configured to distinguish speakersbased on their voices during a communication session may result in theenvironment 6200 being configured to train individual acoustic, languageand other models for individual speakers. For example, if a new voice isdetected by the diarizer 6201, a new model may be created tocharacterize the new speaker's voice. A new set of ASR models may alsobe created and adapted to increase ASR accuracy for that voice. Thevoice (a.k.a. speaker) model may log information for the individualvoice such as acoustic characteristics, favorite topics, vocabulary, andword usage. For each audio segment, the diarizer 6201 may compare thevoice to a voiceprint and determine who is speaking and/or whether thespeaker has changed. In response to a change of voice, in these andother embodiments, additional models may be created. Modifications,additions, or omissions may be made to the environment 6200 withoutdeparting from the scope of the present disclosure.

FIG. 59 illustrates an example environment 6300 for using trainedmodels, in accordance with some embodiments of the present disclosure.In some embodiments, the speech recognition models may be trainedseparately. In these and other embodiments, a source separator 6302 maybe configured to process communication session audio to determinedifferent voices in the communication session audio, such as voices fromparty 1 and party 2, and to separate each voice into a unique audiostream that is provided to ASR systems 6320 for transcription. In someembodiments, a diarizer 6308 may provide information to the sourceseparator 6302 on which speaker is speaking at a given time such thatthe source separator 6302 may generate the unique audio streams.

One example of a source separator 6302 is an echo canceller thatreceives two audio streams or channels, one carrying party 1's voice andone carrying party's 2 voice. In some instances, some of the signal fromone channel may leak into the other channel but may be removed by thesource separator 6302. For example, the source separator 6302 may useadaptive filtering to remove portions of party 1's voice from theparty's 2 channel or vice versa. Another example of a source separator6302 may include a blind source separator. In these and otherembodiments, the source separator 6302 may receive a signal includingtwo or more voices or other sound sources combined together into onechannel and separate the sound sources into separate channels.

In some embodiments, the ASR systems 6320, as described previously, areconfigured to convert speaker audio into one or more transcriptions. Forthe sake of clarity, a single box is depicted, however it iscontemplated that any number of ASR systems may be implemented. In someembodiments, the ASR systems 6320 may increase accuracy by listening tomultiple sides of the conversation. For example, if one speaker says,“When are we meeting?” the ASR systems may estimate that a response fromthe other speaker saying, “Somewhere around four” is more likely than“Someone's at the door,” and use the estimate to increase accuracy ofoutput transcriptions.

In some embodiments, the language model in the ASR systems 6320 mayrepresent conditional probabilities as being dependent on context fromfirst and second parties. For example, the probability of a word may beexpressed as P(word| context1, context2), where context1 is context fromthe first party and context2 is context from the second party. In thisexample, a dual trigram language model may express the conditionalprobability of the word “four” and “door” as P(“four”|context1=“wemeeting”, context2=“at the”) and P(“door”|context1=“we meeting”,context2=“at the”), respectively. Similarly, a neural net language modelmay estimate the probability of one or more words given input to theneural net including the context of words from multiple speakers.

In some embodiments, the ASR systems 6320 may use language models formultiple parties to generate transcriptions. For example, transcriptionsgenerated by the ASR systems 6320 using the voice from a first party maybe performed using party 1 models 6304. In these and other embodiments,transcriptions generated by the ASR systems 6320 using the voice from asecond party may be performed using party 2 models 6306. In someembodiments, the ASR systems 6320 may provide transcriptions of bothparties to party 2 device 6330 and party 1 device 6440. Othercombinations are contemplated.

Modifications, additions, or omissions may be made to the environment6300 without departing from the scope of the present disclosure. Forexample, the party 1 model 6304 and the party 2 model 6306 may becombined into a single joint model that is built based on speech fromboth the party 1 and the party 2. In these and other embodiments, theASR systems 6320 may use the joint model to generate transcriptions ofaudio from both the party 1 and the party 2.

FIG. 60 illustrates an example environment 6400 for selecting datasamples, in accordance with embodiments of the present disclosure. Insome embodiments, the environment 6400 may be configured to select datasamples that may provide a greater benefit in training models to improvetranscription accuracy than other data samples.

Data, such as audio and/or transcriptions, from a transcription unit6414 may be stored in a database data1 6402. Additionally oralternatively, when model training is being performed “on-the-fly,” theenvironment 6400 may disable the database data1 6402 and not retain thedata. The data may be provided to the sample selector 6404.

In some embodiments, the sample selector 6404 may select data that mayprovide a greater benefit in training models based on ASR confidence forthe data. Alternatively or additionally, the sample selector 6404 mayselect data based on factors other than ASR confidence, such as: (1) howmany idle revoiced transcription unit or data transcribers areavailable; and (2) the existence of a preexisting transcription, or thequality of a preexisting transcription.

In some embodiments, the data selected that may provide a greaterbenefit in training models may be configured to be generated using ahigher-accuracy transcription method by the sample selector 6404. Insome embodiments, the data may include audio. In these and otherembodiments, to generate the higher-accuracy transcription, the selectedaudio may be transcribed using a human or other high-accuracy datatranscriber, such as a revoiced transcription unit. The non-selecteddata may be transcribed using an ASR system. In some embodiments, thedata may include a transcription. In these and other embodiments, editsmay be made to the transcription to generate a higher-accuracytranscription. Alternatively or additionally, the data may include atranscription and audio. In these and other embodiments, a newtranscription may be created and/or edits may be made to thetranscription based on the audio to generate a higher-accuracytranscription. The data and/or the higher-accuracy transcriptions may beprovided to ASR model trainer 6408, which may be analogous to the ASRmodel trainer 6108 of FIG. 57.

In some embodiments, the sample selector 6404 may provide information toa selector that may be configured to select between a revoiced ornon-revoiced transcription unit to generate transcriptions for the data.In these and other embodiments, the information may direct the selectorto select a revoiced transcription to handle communication session audiowhen the sample selector 6404 selects the data to have a higher-qualitytranscription. For example, the sample selector 6404 may determine thata communication session being handled by a non-revoiced transcriptionunit may provide a greater benefit in training models to improvetranscription accuracy. Thus, the sample selector 6404 may direct theselector to select a revoiced transcription unit to handle the remainderof the audio from the communication session to generate higher accuracytranscriptions of the remainder of the audio.

In some embodiments, the sample selector 6404 may also provideconfidence information to the model training process so that modeltraining may weigh samples heavier based on the samples having a higherconfidence. For example, the ASR model trainer 6408 may assign a smallerweight to samples having a lower confidence or may exclude the samplesfrom training. Confidence may be determined, for example, using featuresand methods described herein for estimating confidence, accuracy, errorrate, etc.

Additionally or alternatively, the environment 6400 may be configured toselect substantially all transcriptions using speech recognition fortraining of models. In these and other embodiments, environment 6400 maymake exceptions with respect to some audio. For example, the environment6400 may not use audio from emergency communication sessions,higher-priority communication sessions, and communication sessions usedfor data collection.

In some embodiments, revoicing may be used to generate higher-accuracytranscription to train ASR systems. In these and other embodiments,audio used for training may be selected using active learning and sentto a CA client. When transcribing communication sessions for datacollection, the CA client may be configured differently from when the CAclient may transcribe live communication sessions. For example, for datacollection the CA client may allow the CA to (1) rewind and listen toaudio again, (2) lag behind real time, increasing transcription latency,and (3) skip portions of the input audio, such as when the CA getsbehind or when the audio fails to meet specified criteria for usefulnessin model training. In some embodiments, during data collection multiplerevoicing transcription units may be bridged together on a single audiostream to generate multiple transcriptions. The multiple transcriptionsmay be fused to generate an output result. Modifications, additions, oromissions may be made to the environment 6400 without departing from thescope of the present disclosure.

FIG. 61 illustrates an example environment 6500 for training languagemodels, in accordance with some embodiments of the present disclosure.In some embodiments, a transcription unit 6514 may generate atranscription from received audio. In some embodiments, one or more ASRsystems and/or fusers in the transcription unit 6514 may use languagemodels as part of the process of generating the transcriptions.

The transcription and the audio may be stored in a database 6502.Additionally or alternatively, when training of the models is beingperformed “on-the-fly,” the environment 6500 may disable the database6502 and not retain the data or allow the database to only save databriefly during on-the-fly training.

In some embodiments, transcriptions from the database 6502 may beprovided to a denormalizer 6503. The denormalizer 6503 may apply rulesor methods to the transcriptions to convert the transcriptions to aconsistent or near consistent format. The denormalizer 6503 isillustrated as receiving input from the database 6502, however, in someembodiments, the denormalizer 6503 may be configured to receive inputfrom the transcription unit 6514 and output to the database 6502 so thatdenormalized transcriptions are stored.

In some embodiments, the transcriptions may be used to train languagemodels that may be used by the transcription unit 6514. A languagemodeler 6504 may be configured to train language models. In these andother embodiments, the language modeler 6504 may train a language modelbased on n-grams from transcriptions received by the database 6502 ordenormalizer 6503. For example, a language model may be trained based onprobabilities of n-grams. The probabilities of n-grams may be determinedby the language modeler 6504 based on counting n-grams that occur in thetranscriptions received by the language modeler 6504. An example table6506 of probabilities for n-grams is illustrated.

In some embodiments, the language modeler 6504 may include an n-gramcounter 6508 configured to count n-grams. Based on the counts of then-grams, probabilities for the n-grams may be determined. A languagemodel trainer 6510 of the language modeler 6504 may be configured to usen-gram counts and/or n-gram probabilities to train a language model. Thelanguage model trained by the language modeler 6504 may be provided tothe transcription unit 6514.

In some embodiments, n-gram log probabilities may be stored and used inplace of n-gram probabilities. In some embodiments, where there may beinsufficient data or memory storage to count or use a given n-gram oflength n, the n-gram counter 6508 may be configured to use a shortern-gram (for example, one of length n−1). For shorter n-grams, a backoffprobability may be used to modify the language model probability.

An example of the operation of the environment 6500 with respect to anexample phrase is now provided. The transcription unit 6514 mayrecognize the phrase “OK, let's meet downtown at 4:00” from receivedaudio. Using an equivalency table 6520, the denormalizer 6503 maps“4:00” to “four o'clock,” “downtown” is converted to “down town,” and soforth. The denormalizer 6503 outputs the phrase: “ok let's meet downtown at four oclock.” The n-gram counter 6508 updates an n-gram counttable 6512 based on the n-grams in the received phrase. Based on theupdate, the language modeler 6504 may determine that 11,322 occurrencesof “ok let's meet” have been counted. Based on the updated count of then-grams, the language model trainer 6510 may determine that theprobability of the word “meet” given that the preceding words are “oklet's” is 0.11 and the backoff factor is 0.23. Using the updatedprobabilities, the language model trainer 6510 may train a new languagemodel that is provided to the transcription unit 6514. Modifications,additions, or omissions may be made to the environment 6500 withoutdeparting from the scope of the present disclosure.

FIG. 62 illustrates an example environment 6600 for training models inone or more central locations, in accordance with some embodiments ofthe present disclosure. In some embodiments, the environment 6600 mayinclude transcription units 6614 a-c, collectively transcription units6614. Each transcription unit 6614 may include one of ASR systems 6620a-c. The environment 6600 may further include privacy filters 6610 a-c,collectively, the privacy filters 6610 and an ASR training system 6604.

In some embodiments, the transcription units 6614 may obtain audio andgenerate transcriptions of the audio. The transcriptions may be providedto the privacy filters 6610. The privacy filters 6610 may filter thetranscriptions to remove private data and send the remaining data to theASR training system 6604.

In some embodiments, the ASR training system 6604 may include a datacollector 6606 and the ASR trainer 6602. When data storage is permitted,the ASR training system 6604 may further include a database 6608. Thedata from the privacy filters 6610 may be provided to the data collector6606. The data may include transcriptions and data structures. The datamay be provided to the ASR trainer 6602 from the data collector 6606 orthe database 6608. The ASR trainer 6602 may use the data to train ASRmodels. The ASR trainer 6602 may train speaker-independent orspeaker-dependent ASR models. External data, such as commercial data,data from other services, or data from the Internet, may also be used totrain the models.

In some embodiments, the privacy filters 6610 may convert datastructures into forms that are anonymous. The data structures beinganonymous may indicate that information identifying the speaker has beenstripped out and that personal information has been removed. In someembodiments, the privacy filters 6610 may convert data structures intoforms using a process that is a non-reversible. The process beingnon-reversible may indicate that information, such as text and audio,sufficient to reconstruct the audio or content of the conversation hasbeen deleted. In making data nonreversible, the privacy filter 6610 maybe configured to convert data into a format that may be used to trainmodels but cannot easily be used to reconstruct the conversation.Examples of how anonymous, nonreversible data may be created and used totrain an ASR system may include:

-   -   1. A transcription unit or a central server may count n-grams.        N-grams may be used to train language models.    -   2. Model parameters, trained on information extracted from        communication session data before the communication session data        is deleted, are determined or adapted and used to train ASR        models.    -   3. RNNLM (recurrent neural network language model) weights are        adapted to communication session data, then used by the ASR        trainer 6602 to train or adapt an RNNLM.    -   4. Data from substantially one side of the conversation is        stored and used to train models such as acoustic and language        models. Data from one or more other parties is deleted, such as        at the end of the communication session. Alternatively, data        from each party is disassociated such that data samples are        separated from other stored data samples and stored in a format        where the various samples are not linked, that personal        information such as telephone numbers linking captured data to        parties on the communication session is deleted or stored in a        format or location disassociated with the communication session        data, and that information linking the multiple sides of the        conversation is deleted. The ability of an unauthorized person        to reconstruct the conversation by matching silence endpoints to        speech endpoints may be further impeded by adding or deleting        silence in the recorded data and by separating and        disassociating speaker turns, or portions of a conversation        separated by silence or periods when the other party is        speaking.    -   5. Data is collected from transcription units as follows: Deploy        ASR systems in multiple transcription units, where ASR systems        adapt to communication session or revoiced audio as it is        processed. A privacy filter 6004 may convert data to a new        format (e.g., features extracted from audio, intermediate model        parameters, or ASR models) that provides increased privacy. A        data collector 6606 collects data in the new format from each        transcription unit. The ASR trainer 6602 uses the new data to        train or adapt models. Examples of how this may be performed        include:        -   a. Each transcription unit converts audio and text data into            a non-reversible form insufficient for reproducing the            conversation. Example forms include:            -   i. N-grams (see FIG. 63 and item (b), below).            -   ii. Text features extracted from transcriptions of                speech segments, where the temporal order of speech                segments is discarded. Examples of text features may be                n-grams or subword units such as phonemes.            -   iii. Acoustic features such as spectral features                extracted from audio segments, where the temporal order                of speech segments is discarded.            -   iv. In one variation of (ii) and (iii), the text                features and audio features from the same segment may be                associated with each other but disassociated from other                segments.        -   b. Each transcription unit creates and counts n-grams. The            data collector 6606 retrieves and combines n-gram counts            from the transcription units and uses the counts to train a            language model (see FIG. 63).        -   c. Each transcription unit creates or adapts a model based            on communication sessions processed by that transcription            unit. The data collector 6606 retrieves model updates from            the transcription units and forwards them to the ASR trainer            6602. The ASR trainer 6602 uses model updates from the            transcription units to create an ASR model. In some            embodiments, the method for an ASR trainer 6602 using models            from transcription units to train an ASR model includes            averaging model parameters across transcription units. In            some embodiments, the ASR trainer 6602 uses models from            transcription units with an ASR system to process audio to            create a higher-accuracy transcription. The ASR trainer 6602            uses the higher-accuracy transcription to train ASR models.        -   d. Each transcription unit generates and stores temporarily            (a few seconds to a few days) data such as audio and text            data. The ASR trainer 6602 retrieves audio and text data            from the transcription units and uses it to train models. To            preserve privacy, this process may use encryption and may            anonymize, i.e., discard information related to the            speaker's identity or personal information, the data before            it is stored or before it is retrieved by the ASR trainer.            After training on a first batch of data from transcription            units and before training on a second batch of data from            transcription units, the ASR trainer 6602 may delete the            first batch of data.        -   e. Each transcription unit may generate and store            temporarily (a few seconds to a few days) data such as audio            and text data. The transcription units may execute part of            the ASR training process, and forward intermediate results            to the data collector, and then to the ASR trainer 6602,            which completes the training process to train an ASR model.        -   f. If a transcription unit includes a speaker-dependent ASR            system, the transcription unit may upload models or other            files trained for use by the speaker-dependent ASR system to            the ASR trainer 6602, which may use the models or files for            training.    -   6. Statistics from speech or text segments, where the order of        the segments has been deleted, is stored. These statistics are        used to train models.

An example of using statistics to train models includes exampleoperations provided below which may be performed by the components ofthe environment 6600:

-   -   1. Divide audio and/or text data into segments. The segment        boundaries may be defined in terms of:        -   a. A specified length of time.        -   b. A specified number of speech analysis frames (i.e., a            period of time, usually 5-40 ms during which speech is            considered to be relatively constant, frames may overlap,            frame rate is the distance or time between centers of            adjacent frames).        -   c. A specified number of syllables.        -   d. A specified number of words or phrases (e.g., n-grams).            For example, a segment may include a sequence of six words            (i.e., n=6).        -   e. A specified number of subword units such as phonemes. For            example, a segment may include a sequence of features            extracted across three phonemes.        -   f. A specified number of speaker turns (i.e., a segment of            time where one party speaks) as determined by, for example,            periods of silence and/or where another party speaks.    -   2. Extract features (e.g., n-grams, spectral features, neural        net weight values) from multiple segments.    -   3. Optionally filter data to remove sensitive information, such        as redacted data.    -   4. Delete at least some information related to the order,        association (such as a communication session identifier), caller        identity, or sequential position of each segment. For example,        relative temporal information or timestamps may be deleted so        that the segments cannot be restored to their original order. In        another example, n-grams may be counted or created and stored,        but the order in which the n-grams appeared, any communication        session identifiers, and the caller identity may be discarded.        In another example, a series of features spanning a series of        adjacent speech analysis frames in a segment may be saved, but        the temporal relationship of this segment with respect to other        segments may be deleted.    -   5. Store the extracted features (minus deleted and redacted        information).    -   6. Discard the original audio and text.    -   7. Use retained data to train or adapt a model. For example, for        each segment:        -   a. Identify a model element where a segment may be an            example, counterexample, or otherwise useful in training the            model element.        -   b. Train the model elements based on the segment.    -   8. Once multiple segments have been used to adapt a model, the        model may be distributed to multiple ASR systems, such as ASR        systems used by transcription units, where it may be used to        transcribe speech for multiple speakers.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations and actions, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

The process of training new models may be on-going, continuous,periodic, random, or it may be initiated in other ways. For example, theprocess may be initiated in response to data being received fromtranscription units, to a schedule, or to external data being received.The environment 6600 illustrates an embodiment where ASR systems andprivacy filters 6004 are tied to the transcription unit 6614, but atleast some of these components may be implemented elsewhere such as atthe ASR training system 6604, which may be located on the same localnetwork as the transcription units 6614 or remotely, such as at one ormore central data processing sites. Modifications, additions, oromissions may be made to the environment 6600 without departing from thescope of the present disclosure.

FIG. 63 is a flowchart of an example method 6700 of collecting and usingn-grams to train a language model, in accordance with some embodimentsof the present disclosure. The method 6700 may be arranged in accordancewith at least one embodiment described in the present disclosure. Themethod 6700 may be performed, in some embodiments, by processing logicthat may include hardware (circuitry, dedicated logic, etc.), software(such as is run on a general-purpose computer system or a dedicatedmachine), or a combination of both. In some embodiments, the method maybe performed by language modeler 6504 of FIG. 61. The language modeler6504 may include other components such as the denormalizer 6503 of FIG.61 and the privacy filter 6610 of FIG. 62. In these and otherembodiments, the method 6700 may be performed based on the execution ofinstructions stored on one or more non-transitory computer-readablemedia. Although illustrated as discrete blocks, various blocks may bedivided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

The method 6700 may begin at block 6702 where a communication sessiontranscription may be received. At block 6704, the communication sessiontranscription or a portion of the communication session transcriptionmay be denormalized. In some embodiments, it may be determined if thecommunication session transcription is already in a consistent ordenormalized format. In response to the communication sessiontranscription is already in a consistent format, the block 6704 may beskipped.

At block 6706, one or more n-grams may be extracted from thecommunication session transcription. In some embodiments, n-grams may beextracted by identifying a segment of words in the communication sessiontranscription where the segment length is at most n words. An examplevalue for n may be in the range of between about three and ten, althoughany reasonable number of words may be selected. In some embodiments, fora given value of n, segments of length n−1, n−2, n−3, and so on to alength of one word may also be identified. Each segment may bedesignated as an n-gram. The n-gram may be extracted from thecommunication session transcription before the portion of thetranscription including the n-gram is deleted or overwritten.

At block 6708, it may be determined for each extracted n-gram if then-gram exists in the n-gram table. An n-gram table includes a list ofn-grams previously identified and counters, where each of the countersis associated with a different one of the n-grams. The values of thecounters may indicate the number of occurrences for each n-gram. Inresponse to the n-gram existing in the table, the corresponding counteris incremented at block 6710. In response to the n-gram not existing inthe table, the new n-gram is designated as a candidate n-gram and themethod proceeds to block 6712.

In some embodiments, an initial n-gram table may have been previouslybuilt using transcriptions. In these and other embodiments, the initialn-gram table may be used. Alternatively or additionally, an initialn-gram table may not exist. In these and other embodiments, the initialn-gram table may be built and the counters may be set to zero, anothervalue, or random values for each n-gram. In some embodiments, an initialn-gram table may include n-gram counter values derived from a textcorpus. The text corpus may be derived from text generated by atranscription service, text generated by another service, a publiclyavailable corpus or database, information downloaded from websites, orother corpus.

At block 6712, the candidate n-gram may be filtered. In someembodiments, filtering the n-grams may include deleting or redactingpersonal information, private information, personally identifiableinformation, or other sensitive information. In some embodiments, ann-gram may be filtered if the n-gram includes at least one specifiedcombination of information. An example of a specified combination ofinformation may be a (a) name, street address, or driver's licensenumber; and (b) information about the individual's health, medical care,financial accounts, or credit history. Another example of a specifiedcombination of information may be (a) a person's financial accountnumber; and (b) an associated password, PIN, security code. Othercombinations may include combinations that specify two or more groups ofinformation that, if items from all groups are included in the n-gram,the n-gram may be a candidate for redaction. Multiple combinations maybe specified and the n-gram may be filtered if the n-gram includes anyof the specified combinations. For example, a candidate n-gram “David'sPIN is 1234” may be identified as a filter candidate because it containsa name and a PIN. The n-gram may be deleted, filtered, or otherwiseprocessed using one or more of several methods, including:

-   -   1. N-gram may be deleted so that the candidate n-gram (“David's        PIN is 1234”) is not added to the n-gram table.    -   2. The n-gram may be filtered by deleting sensitive words or        phrases and the redacted n-gram may be added to the n-gram        table.

The redacted term(s) may be replaced with a tag such as “_redacted_”(for example “_filtered_'s PIN is 1234” or “David's PIN is _redacted_”).

-   -   3. The n-gram may be filtered by replacing sensitive words or        phrases with class tags. A class may be a name, account number,        digit, medication name, diagnosis, etc. Class tags may be        characters or strings that represent the class and are not        expected to occur otherwise in the text. For example, a name may        be replaced with “_name_,” _firstname_,” or “_lastname_” (e.g.,        “_firstname_'s PIN is 1234”), a digit string with “_string_”        (e.g., “David's PIN is _string_”), a digit with “_digit_” (e.g.,        “David's PIN is _digit__digit__digit__digit_”), a medication        with “_medication_,” etc. Class tags may be used to train        class-based models such as hierarchical statistical language        models. Examples of methods to replace sensitive information        with class tags include:        -   a. In a variation on replacing words with class tags, class            member frequencies may be tracked in the n-gram table or in            a separate table. For example, if “David” is replaced with            “_name_,” the counter for the unigram “David” may be            incremented or n-gram counter 6508 may keep a separate table            for filtered names, each with an associated counter.        -   b. In another variation on replacing words with class tags,            classes may be subdivided into subclasses to create more            classes. Subdivision may be performed by defining specific            subclasses. For example, names may be divided into male            names and female names and classes “_male_name_” and            “female_name_” may be created. Alternatively, subdivision            may be performed in a data-driven model such as clustering.            For example, a natural language processing method may            examine how names are used in context and determine that            there are 50 classes of names where each class tends to            appear over a range of contexts, so that classes such as            “_name1_,” “_name2_,” “_name3_,” etc., may be defined. If            “David” is a member of the _name23_class, then “David” may            be replaced by “_name23_” in the n-gram.    -   c. Class-based n-grams may be upgraded to regular n-grams under        selected conditions, such as based on frequency of occurrence.        For example, if the n-gram “David Thomson has cancer” is used to        create or increment a class-based n-gram        “_firstname__lastname_has cancer,” and it is determined that a        counter for the class-based n-gram exceeds a selected threshold,        then the class-based n-gram may be flagged as eligible for        provisional regular n-gram status, meaning, for example, that        “David Thomson has cancer” (and other such n-grams containing        real names) may be created and counted temporarily. If “David        Thomson has cancer” accumulates over a specified threshold, such        as 100 counts in a month, then it may be established as a        regular n-gram, not subject to filtering.        -   d. The n-gram may be filtered by redacting enough sensitive            information that the n-gram no longer contains a specified            combination of information. For example, if an n-gram            contains a personally identifiable word such as a name and a            piece of personal information such as a password, the n-gram            may be filtered by redacting only the name or only the            personal information. Additionally or alternatively, the            n-gram may be filtered by redacting all sensitive            information in the n-gram.    -   4. The n-gram counter may be incremented in the n-gram table if        the n-gram or its filtered version already exists. In some        embodiments, multiple n-gram counters may be incremented, for        example when there are multiple redacted forms. For example, if        “David's PIN is 1234” is spoken and the n-gram is already in the        n-gram table, its associated counter may be incremented. If        “_name_'s PIN is _filtered_” is also in the n-gram table, its        associated counter may be incremented as well. If the unigram        “David” is in the table, it may also be incremented.    -   5. The value of n may be reduced for a particular n-gram so that        sensitive information is effectively removed. For example, the        candidate 4-gram “David Thomson has cancer” may be deleted and        only the 3-grams, “David Thomson has” and “Thomson has cancer”        may be kept.    -   6. Potentially sensitive n-grams may be stored in a secure or        temporary location such as on the CA workstation or on a server        local to ASR servers or CA workstations for a specified        duration. If a selected number of instances of an n-gram have        not been counted during that duration, the n-gram may be        deleted. For example, “David Thomson has cancer” may be added to        a provisional n-gram table and counted, but if, at a specified        deadline such as after a week, the associated counter is less        than 100, the n-gram may be removed. The minimum number of        instances and deletion deadlines may vary according to the        n-gram length and the type of potentially sensitive information        contained.    -   7. Potentially sensitive n-grams may be stored in an n-gram        table, but only forwarded to a language model trainer if the        n-gram meets specified criteria such as if the associated count        is greater than a selected threshold. This approach may limit        risk by keeping potentially sensitive n-grams in one location        (an n-gram table or a temporary n-gram table) and away from        other locations such as model training sites.

A number of criteria may be evaluated when determining whether a term(one or more words) in an n-gram is sensitive, including one or more of:

-   -   1. The term may be a name in a selected format, which may be one        or more of:        -   a. The term may be a name.        -   b. The term may be a first name.        -   c. The term may be a last name.        -   d. The term may be a first and last name.        -   e. The term may be a first name immediately followed by a            last name.    -   2. The term may be an account number or other potentially        private numeric sequence. A string of digits may be determined        to be sensitive in one or more of multiple ways, including:        -   a. The string contains at least M digits. The value of M may            be, for example, four for detecting PINs.        -   b. The string of digits is wholly contained within the            n-gram. For example, in the sentence “David's PIN is 1234,”            the n-gram “PIN is 123” may not be considered sensitive,            according to this criterion, because part of the pin lies            outside of the n-gram, whereas the n-gram “is 1234” may be            considered sensitive.        -   c. The string of digits has a length of at least M and the            n-gram also contains the word “PIN.”        -   d. The string of digits has a length of at least M and is            wholly contained in the n-gram.        -   e. The string of digits matches an entry in a table, such as            a table of account numbers or telephone numbers.        -   f. The string length and format matches that of a telephone            number. This criterion may be narrowed by requiring the            n-gram to also contain one or more fixed phrases such as “my            number is” by the speaker or one or more fixed preceding            phrases such as “your number please” spoken by another party            on the communication session.        -   g. The string length and format matches that of an            identification number such as a government-issued ID number.            This criterion may be narrowed by requiring the n-gram to            also contain one or more fixed phrases such as “driver's            license number” by the speaker or one or more fixed            preceding phrases such as “your social security number            please” spoken by another party on the communication            session.        -   h. The string length and format matches that of a credit or            debit card number. For example, if the initial digits of a            digit string match those known to be associated with card            numbers issued by a particular financial institution and if            the string length is consistent with a card number, the            format may be considered a match.    -   3. The term may be an email address.    -   4. The term may be all or part of a mailing address or current        location.    -   5. The term may be a driver's license number or national ID        number.    -   6. The term may be a license plate number.    -   7. The term may be a social security number or the last four        digits of a social security number.    -   8. The term may be a health insurance number.    -   9. The term may be an IP address.    -   10. The term may be the name of a drug such as a prescription        drug.    -   11. The term may contain information about a financial account        such as an account number.    -   12. The term may contain information about a person's credit        history or capacity.    -   13. The term may be a security or access code.    -   14. The term may be a monetary value related to a financial        transaction or account balance.    -   15. The term may be a password.    -   16. The term may be a security question and/or the answer.    -   17. The term may be a digit string of a specific length or        minimum length.    -   18. The term may be a string of digits spoken in a specified        natural number format such as “thirty-six twenty-two” (as is        commonly done with the last four digits of social security        numbers) rather than “three six two two” or with pauses between        sets of digits representing, for example, spaces or dashes        between groups of numbers, such as in a credit card number.    -   19. The term may be an indication of religious views, practices,        or affiliation, biometric information, political opinion or        affiliation, gender or gender identity, sexual preference or        activity, genetic information, health status, status of vision        or hearing, ethnicity or birth place, race, or nationality.    -   20. The term may be found on a list of common medical or        financial terms.    -   21. The term may be found on a list of terms such as “PIN,”        “credit,” “debit,” “password,” “user,” “username,” “dollars,”        etc., designated as potentially related to sensitive        information.    -   22. The term may be the name of a disease, information about        medical care or medical condition (including mental health or        substance abuse), or a diagnosis. This determination may, for        example, be based on a lookup table of diseases, medical        conditions, and diagnoses.    -   23. The term may be a name and one or more other pieces of        sensitive information such as other examples of sensitive        information from this list.    -   24. A corpus of text may be labeled to mark instances of        sensitive information. A machine learning method, such as        logistic regression or deep neural network training or another        method from Table 9, may process the marked corpus to learn        patterns associated with sensitive information and to create a        sensitive information model. Once the model is created, a        classifier may use the sensitive information model to identify        n-grams likely to contain sensitive information.    -   25. The n-gram may contain at least one specified combination of        sensitive information, where sensitive information may be one or        more of the items listed above.

Since it may not be known whether a term such as a digit string isactually an account number, security code, or other potentiallysensitive term, a term may be determined to be sensitive if its formatmatches one or more of the formats described in the list above. Forexample, the number 123-456-7890 may be determined to be sensitivebecause it matches the format of a telephone number, even though aprivacy filter may not know whether it is a real telephone number. Insome embodiments, a privacy filter may determine that a word issensitive by comparing it to a table of examples, such as a list ofnames, medical conditions, key words, etc. Additionally oralternatively, a privacy filter may determine that a word is sensitivefrom the word in context, which may include one or more words that comebefore and/or after the word in question. The context may includecapitalization and/or punctuation. For example, a neural net, logisticregression, or other classifier such as the examples in Table 9 mayinput the word and its context and determine that the word is sensitivebased on how the word is used. The filter may determine, for example,that “bill” is a name in “I'm Bill Johnson,” but not in “The bill is toohigh.” In some embodiments, the classifier may be trained on a corpus oftext where words are tagged according to their membership in a classsuch as medical conditions, dmg names, etc.

The determination at block 6712 that an n-gram contains one or morepieces of information from the list above may be based on a format orlookup table. For example, the determination that a word is a name maybe based on lookup tables containing first and last names.Alternatively, the determination may be based on capitalization of theword, the context in which the word appears, or other criteria. Forsecurity and privacy, audio, text, logs, billing records, n-grams,statistics, and other data forms derived from communication session datasuch as communication session data or calling information that maycontain potentially sensitive information may be filtered for privacy,encrypted, held behind firewalls, protected with passwords, andrestricted to access by a limited group of people. The security measuresmay apply to communication session data that is stored, statistics andother information derived from communication session data, and modelsbuilt using communication session data.

At block 6714, a new n-gram may be created and inserted into the n-gramtable. Additionally or alternatively, if creating new n-grams isconsidered a potential privacy risk, the processing logic may skip block6714, and only count existing n-grams. If the new or candidate n-gramstill exists after filtering for privacy, the n-gram may be added to then-gram table and the corresponding counter may be set to 1, at block6710. In some embodiments, when creating a new n-gram, an n-gram recordmay be created that includes the text of the n-gram and one or morecounters. The new n-grams record may also include additional data fieldssuch as a timestamp indicating a time and date of creation. The creationtimestamp may be used, for example, in determining whether the n-gram isto be deleted or filtered. A timestamp may take on any of severalformats, including the date and time of creation, the number of alltypes of n-grams or certain types of n-grams counted at the time orsince the n-gram was created, the number of seconds since the datacollection began, etc. By way of example, the column labeled “N” (forexample, the number of n-grams counted since the n-gram was created) inTable 16 below illustrates an example of a timestamp in the n-gramtable.

TABLE 16 Count Phrase N 11,322 o k let's meet 11,204,199 3,250 k let'smeet down 7,290,022 19,394 let's meet down town 11,204,204 8,044 meetdown town at 11,203,202 5,204 down town at four 11,201,266 1,902 town atfour oclock 10,292,338

Timestamps may be used, when training a language model to adjust for theduration of time over which an n-gram has been collected. For example,if one n-gram has been counted/detected for over a year and anothern-gram was created only one day ago, the relative time spans may betaken into account when estimating conditional probabilities and fortraining a language model. An example of how this may be done is nowprovided.

Suppose a trigram, an n-gram where n=3, includes words w1, w2, w3. Theconditional probability of w3 given w1 and w2 is P(w3|w1,w2) and may bedetermined as:

$\begin{matrix}{{P\left( {\left. {w\; 3} \middle| {w\; 1} \right.,{w\; 2}} \right)} = \frac{P\left( {{w\; 1},{w\; 2},{w\; 3}} \right)}{P\left( {{w\; 1},{w\; 2}} \right)}} \\{{= \frac{{count}\left( {{w\; 1},{w\; 2},{w\; 3}} \right)}{{count}\; \left( {{w\; 1},{w\; 2}} \right)}},}\end{matrix}$

where count(x) is the value of an n-gram counter associated with n-gramx. The language model entry for the n-gram (w1,w2,w3) may include theabove conditional probability or a variation thereof such as a logconditional probability.

To account for collection time durations, a first timestamp may bedefined as time T(w1,w2,w3) that corresponds to the number of n-grams ofall types that have been created or counted since n-gram (w1,w2,w3) wascreated. A second timestamp may be defined as T(w1,w2) that correspondsto the number of n-grams of all types that have been created or countedsince n-gram (w1,w2) was created. The timestamp-adjusted form of theconditional probability may be expressed as

${P\left( {\left. {w\; 3} \middle| {w\; 1} \right.,{w\; 2}} \right)} = {\frac{{{count}\left( {{w\; 1},{w\; 2},{w\; 3}} \right)}/{T\left( {{w\; 1},{w\; 2},{w\; 3}} \right)}}{{count}\; {\left( {{w\; 1},{w\; 2}} \right)/{T\left( {{w\; 1},{w\; 2}} \right)}}}.}$

Advantageously, T(w1,w2,w3) may be stored in the record for n-gram(w1,w2,w3) and T(w1,w2) may be stored in the record for n-gram (w1,w2).

Additionally or alternatively, collection time durations may beaccounted for by creating a counter for each n-gram and for an n-gramcorresponding to the first n−1 words in the n-gram. For example, arecord for an n-gram (w1,w2,w3) may include a first counter c3 thatindicates how many times (w1,w2,w3) has been counted since (w1,w2,w3)was created, and a second counter c2 for (w1,w2) that indicates how manytimes (w1,w2) has been counted since (w1,w2,w3) was created. Wheneverthe n-gram (w1,w2,w3) is detected, c3 may be incremented. Counter c2 maybe incremented whenever the n-gram (w1,w2) is detected. Thetimestamp-adjusted form of the conditional probability may then bedetermined as

${P\left( {{{w\; 3}{w\; 1}},{w\; 2}} \right)} = {\frac{c\; 3}{c\; 2}.}$

The conditional probability expressed in the above equation may bemodified for use in a language model. A language model probability maybe derived from the above conditional probability, for example bynormalizing the probability by multiplying by a constant or applying afunction, by taking the log probability, and/or by interpolating thelanguage model with other language models. Other factors may be includedsuch as multipliers for backoff probabilities and adjustments for caseswhere counters are too small to provide good estimates.

In some embodiments, if new or candidate n-grams are not used to createn-grams in the n-gram table, the candidate n-grams may be deleted. Thesteps described above may be performed for each n-gram in atranscription, and when complete, all existing copies of thecommunication session transcription (including normalized anddenormalized versions) used to extract the n-grams may be deleted.Transcriptions, portions of a transcription, and candidate n-grams maybe deleted by the processing logic under one or more conditionsincluding:

-   -   1. When the transcriptions are no longer needed to provide        transcriptions.    -   2. When the communication session ends. The end of the        communication session may variously be defined as the point        where one party disconnects, both parties disconnect, or when        transcriptions are still being provided to or generated for at        least one party.    -   3. Within a selected time, such as within a predetermined number        of seconds after a communication session ends.    -   4. After selected criteria have been met, such as when the        transcription system has had time to discontinue processing for        the communication session.    -   5. When the transcriptions are no longer needed for training,        such as to update an n-gram table. For example, a portion of a        transcription may be deleted once all candidate n-grams have        been extracted from the portion of the transcription, and the        candidate n-grams may be deleted once they have been used to        update the n-gram table.

At block 6716, a language model may be built using n-grams and countersin the n-gram table. The language model may be used, for example, by oneor more ASR systems, interpolators, and fusers and to train otherlanguage models. At block 6718, the language model being used by ASRsystems may be updated with the newly built language model.

In some embodiments, as discussed previously, the n-gram candidates maybe filtered. In these and other embodiments, the n-gram candidate may beexamined to determine information that may be filtered from the n-gramcandidate. In some embodiments, the information that may be determinedto be filtered may be sensitive information. The term “sensitive” asused here may include personal information, private information,confidential information, personally identifiable information (PII),sensitive personal information (SPI), etc. Examples of sensitiveinformation may include passport number, date/place of birth, login nameor screenname or handle, zip code, state (e.g. Idaho), mother's maidenname, dollar amount of an account balance or previous financialtransaction, criminal record, grades, salary amount, or biometrics(face, handwriting).

In response to an n-gram candidate being determined to not includesensitive information, no information may be filtered from the n-gramcandidate. In these and other embodiments, the n-gram candidate, may beused to create regular n-gram entries in a regular n-gram table witheach regular n-gram entry corresponding to a counter that may beincremented as n-grams associated with the counters are encountered intranscriptions as described in block 6706. In response to an n-gramcandidate including sensitive information such that the n-gram candidatemay be filtered, the n-gram candidate may filtered.

In some embodiments, filtering an n-gram candidate may include deletingall of the sensitive information in the n-gram candidate, deleting someof the sensitive information to render the n-gram candidate notsensitive, deleting the entire n-gram, replacing all of the sensitiveinformation, such as terms that are determined to be sensitive, withclass tags associated with the terms, or replacing some of the sensitiveinformation with class tags to render the n-gram candidate notsensitive. Filtering the n-gram candidates may result in filteredn-grams. The filtered n-grams may be used to train a language model forASR systems. Examples of various methods that may use filtered n-gramsto train a language model are now provided.

For example, one method may use the filtered n-grams to build hierarchallanguage models with a “top” grammar and one or more sub-grammars. In ahierarchal model, the top grammar may include a normal statisticallanguage model trained from text. In these and other embodiments, classtags that may be included in filtered n-grams may be defined to link thetop grammar to sub-grammars that represent classes. For example,consider an n-gram “John is a Democrat.” The potentially sensitiveinformation in the n-gram may include the terms “John” and “Democrat.”The sensitive terms may be replaced with tags. The tags may beassociated with classes. For example, the name John may be replaced witha tag of “first” that may be associated with the class for first namesand the term “Democrat” may be replaced with a tag of “party” that maybe associated with a class for political parties. The filtered n-grammay be created as follows “first is a_party_.” In these and otherembodiments, a top grammar may be built or adjusted to include theclasses associated with the tags “_first_” and “_party_.” Alternativelyor additionally, two sub-grammars of the top grammar may be built thatmay each be associated with one of the new classes. For example, a firstsub-grammar may be one named “_first_,” and may include unigrams ofnames such as, “david,” “john,” “mike,” etc. A second sub-grammar may benamed “_party_,” and may include unigrams of different politicalparties, “democrat,” “republican,” “libertarian,” etc.

In some embodiments, creating and counting n-grams may include countingunigrams, since collecting n-grams of length n may include collectingn-grams of length n−1, n−2, n−3, . . . , 1. As a result, a regularn-gram table may, assuming single terms are not sensitive, includeunigrams and probabilities for terms (e.g. “david” and “democrat”) ofeach class associated with the two sub-grammars.

In some embodiments, an ASR system may be configured to determineprobabilities for n-grams including sensitive terms by using languagemodels combined in the hierarchal structure as discussed above. In theseand other embodiments, the probability of an n-gram may be determinedfrom the probability of the filtered n-gram multiplied by theprobability of a filtered term in the n-gram, given the term'smembership in the class. For example, if the probability that a person'sfirst name is john is P(“john”|first_)=0.017, the probability that aperson is a Democrat is P(“democrat”|_party)=0.31, and the probabilityof the filtered n-gram is P(“_first_ is a _party_”) is 0.00001, then theASR system may estimate the probability of the n-gram “john is ademocrat” to be

${P\left( {``{{John}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Democrat}}"} \right)} = {{{P\left( {``{{\_ first}{\_ is}\mspace{14mu} {a\_ party}\_}"} \right)}*{P\left( {{``{john}"}{{\_ first}\_}} \right)}*{P\left( {{``{democrat}"}{{\_ party}\_}} \right)}} = {{0.00001*0.017*0.31} = {5.27e\text{-}08.}}}$

For added security, in some embodiments, the probability of an n-gramestimated from existing n-grams or language models may be multiplied byand/or added to a random value. Random numbers may be multiplied and/oradded during n-gram creation and/or updating. In the example above, ifr1 and r2 are random numbers, an n-gram probability may be multipliedand added to random numbers as,

P(“John is a Democrat”)=r1*[Pr_first isa_party_”)*P(“john”|_first_)*P(“democrat”|_party_]+r2

Additionally or alternatively, probabilities may be estimated usingcounters. For example, the probability of a term in a class may bedetermined using the counter for the term divided by a counter for theclass. The probability of an n-gram may likewise be calculated from then-gram counter divided by the total number (T) of n-gram counts. Forexample, suppose 1000 members of the name_ class have been observed andcounted, the name “john” has been counted 17 times, 100 members of the_party_ class have been counted, and the word “democrat” has beencounted 31 times. Suppose further, out of one billion n-grams, then-gram “_first_ is a _party_” has been counted 10,000 times. Theprobability of the n-gram “john is a democrat” may be determined as

${P\left( {``{{John}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Democrat}}"} \right)} = {{{{count}\left( {``{{\_ first}{\_ is}\mspace{14mu} {a\_ party}\_}"} \right)}\text{/}T*{count}\; \left( {{``{john}"}{{\_ first}\_}} \right)\text{/}{{count}\left( {{\_ first}\_} \right)}*{count}\; \left( {{``{democrat}"}{{\_ party}\_}} \right)\text{/}{{{count}({\_ party})}.}} = {{10\text{,}000\text{/}1\text{,}000\text{,}000\text{,}000*17\text{/}1000*31\text{/}100} = {5.27e\text{-}08.}}}$

Additionally or alternatively, the language model may contain logprobabilities and multiplication steps may be implemented as a summationof log values.

In some embodiments, terms in a class may include not only unigrams(n=1), but also n-grams of other lengths where n>one. For values of ngreater than one, n-gram probabilities may be determined as above bymultiplying the filtered n-gram probability by the class probability.For example, the probability of the n-gram “David Thomson PIN 1234,”where a full name may be a class, may be determined as P(“David ThomsonPIN 1234”)=P(_fullname_“PIN 1234”)*P(“David Thomson”|_fullname_).

Another method for using filtered n-grams in ASR systems for generatingtranscription may include determining n-gram probabilities usingfiltered n-grams and storing them in a “fake” n-gram table or languagemodel. The fake n-gram table may contain probabilities of n-grams thatmay have not been observed, such as n-grams that have not been extractedfrom a transcription as described in block 6706. In these and otherembodiments, probabilities of n-grams that may have not been observedmay be included in a fake n-gram table when the n-grams may be predictedto be relatively likely to occur.

Various methods for creating a fake n-gram table are now provided. Forexample, in some methods the n-grams may be generated by using the mostlikely (based, for example, on unigram probabilities) filtered termsfrom each class and combining them with filtered n-grams. Following theexample from above, a fake n-gram table may be constructed by combiningthe filtered n-gram “_first is a _party_” with the 1000 most likelywords in the _names_ class and the 20 most likely words in the _party_class to create 20,000 fake n-grams for “_first_ is a _party_.” Theprobability for each fake n-gram may be determined as above.

As another example, in some methods the existing n-grams or languagemodels, including, for example, one or more regular, filtered, and (ifthere are any) fake n-gram tables, may be used to generate a random textcorpus and the text may be used (e.g. by counting n-grams in the text)to create a fake n-gram table or to add new fake n-gram entries when afake n-gram table already exists. As the random text corpus is beingbuilt, it may be determined if the frequency of occurrence of n-grams inthe random text corpus matches the counts or probabilities of existingn-grams. In response to the frequency of occurrence of n-grams in therandom text corpus not matching the counts or probabilities of existingn-grams, probabilities of n-grams being used to create the corpus may beadjusted so that n-gram frequencies of the final corpus may match orsubstantially match probabilities in the existing n-gram tables.

In some embodiments, the size of a fake n-gram table may be managed byincluding entries with a probability or count above a selectedthreshold. Additionally or alternatively, an auditor may periodicallyexamine the fake n-gram table to remove n-grams based on criteria suchas removing n-grams with probabilities below a selected threshold.Additional methods for pruning or compressing a language model may beused to prune or compress the fake n-gram table.

In some embodiments, filtered n-grams may be used to build a fake n-gramtable as described above, but the counters may be initialized to zero.In these and other embodiments, the counters for the fake n-grams may beincremented in response to the fake n-grams being extracted intranscriptions as described with respect to the method 6700.Additionally or alternatively, the counter for each fake n-gram may beinitialized to a random number instead of to zero. In these and otherembodiments, initializing the counters to random numbers may obscurewhich counters have been incremented since the counters were created.Obscuring which counters have been incremented since the counters werecreated may provide one method to help protect the privacy of thesensitive information in the n-grams.

In some embodiments, the distribution of random numbers applied to thecounters of the fake n-grams may be uniform, normal, Poisson, Cauchy,exponential, geometric, or binomial, among other distributions. In someembodiments, the distribution mean may be zero. In these and otherembodiments, when a random number generator is used to generate initialn-gram counts and produces a negative number, the counter may be set tozero or it may be set to a negative value for counting, then set to zerowhen training a language model.

As another example, in some method filtered n-grams may be used tocreate a fake n-gram table may include using existing n-grams orlanguage models to predict a value for each fake n-gram counter providein the fake n-gram table. For example, let T be the total number ofn-grams of all types with length n observed. T may alternatively be thenumber of words observed so far or the total counts of all regular andfiltered n-grams. When a fake n-gram is created, the counter associatedwith the fake n-gram may be set to an estimated value. The estimatedvalue may be equal to T multiplied by the estimated fake n-gramprobability. For example, suppose one billion n-grams have been observedat the time a new fake n-gram “john is a democrat” is created. Thecounter of the fake n-gram may be initialized to:

${{count}\left( {``{{john}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {democrat}}"} \right)} = {{T*{P\left( {``{{\_ first}{\_ is}\mspace{14mu} {a\_ party}\_}"} \right)}*{P\left( {{``{john}"}{{\_ first}\_}} \right)}*{P\left( {{``{democrat}"}{{\_ party}\_}} \right)}} = {{1\text{,}000\text{,}000\text{,}000*0.00001*0.017*0.31} = 52.7}}$

Alternatively or additionally, the counter of a fake n-gram may beinitialized based on the counts of existing counters instead ofprobabilities. For example, the counter of a fake n-gram may bedetermined by multiplying the counter associated with the filteredn-gram by the probabilities of each word in each class in the filteredn-gram. For example,

${{count}\left( {``{{john}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {democrat}}"} \right)} = {{{{count}\left( {``{{\_ first}{\_ is}\mspace{14mu} {a\_ party}\_}"} \right)}*{count}\; \left( {{``{john}"}{{\_ first}\_}} \right)\text{/}{count}\mspace{11mu} \left( {{\_ first}\_} \right)*{{count}\left( {{``{democrat}"}{{\_ party}\_}} \right)}\text{/}{{{count}\left( {{\_ party}\_} \right)}.}} = {{10\text{,}000*17\text{/}1000*31\text{/}100} = 52.7}}$

Alternatively or additionally, the counter of a fake n-gram may beinitialized based on the counts of existing counters and the totalnumber of words. For example, the counter of a fake n-gram may bedetermined by multiplying the probability of the filtered n-gram by Tand by the counter of each word in the class, divided by the classcounter. For example,

${{count}\left( {``{{john}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {democrat}}"} \right)} = {{T*{P\left( {``{{\_ first}{\_ is}\mspace{14mu} {a\_ party}\_}"} \right)}*{count}\; \left( {{``{john}"}{{\_ first}\_}} \right)\text{/}{{count}\left( {{\_ first}\_} \right)}*{count}\; \left( {{``{democrat}"}{{\_ party}\_}} \right)\text{/}{{{count}\left( {{\_ party}\_} \right)}.}} = {{1\text{,}000\text{,}000\text{,}000*0.00001*17\text{/}1000*31\text{/}100} = 5.27}}$

In some embodiments, n-gram counters may be rounded to integers or mayremain as floating point numbers during creation and during counting.After creation, n-gram counters may be incremented as the n-grams areobserved as described in the method 6700. Additionally or alternatively,n-gram counters may not be incremented and may be periodically updatedusing existing probabilities or counters as described above.

In some embodiments, instead of tracking a counter for each n-gram, fakeor real, a probability of the n-grams may be tracked. In these and otherembodiments, an initial n-gram probability may be determined, forexample as described above. After determining an initial n-gramprobability, the probability for a tracked n-gram may be updated eachtime any n-gram is observed by decreasing the probability slightly whenthe observed n-gram is not the tracked n-gram and increasing theprobability slightly when the tracked n-gram is observed. In these andother embodiments, adjusting the probability of a tracked n-gram may beaccomplished by, each time a new n-gram is observed, multiplying thetracked n-gram by a number slightly less than one, such as (1-u), whereu is an update rate, and adding a very small number (u) when the trackedn-gram is observed. In some embodiments, to simplify the update processand use fewer resources, such as computing resources, tracking mayhappen in batches, for example by applying the update after a number(e.g. 1000) n-grams have been observed in production. In these and otherembodiments, u may be a very small number such as 1/T. An example is nowprovided. Suppose an initial probability for “john is a democrat” isdetermined. Thereafter, each time an n-gram is observed, the “john is ademocrat” n-gram probability may be updated as:

-   -   If the observed n-gram is “john is a democrat”: P(“john is a        democrat”)←(1−u)*P(“john is a democrat”)+u    -   If the observed n-gram is not “john is a democrat”: P(“john is a        democrat”)←(1−u)*john is a democrat”)

In some embodiments, the update term may incorporate a second estimate,P2, of the n-gram probability. This second estimate may be determinedusing any method, such as those described above, used for creating theinitial n-gram probability. The update term may then be the weightedaverage of the update term above and the second estimate, where B is theweighting factor. In some embodiments, B may be a number between zeroand one. For example,

-   -   If the observed n-gram is “john is a democrat”: P(“john is a        democrat”)←(1−B)*[(1−u)*P(′ john is a democrat”]+u+B*P2(“john is        a democrat”)    -   If the observed n-gram is not “john is a democrat”: P(“john is a        democrat”)←(1−B)*[(1−u)*P(“john is a democrat”)]+B*P2(“john is a        democrat”)

In some embodiments, the regular (non-filtered), filtered, and faken-gram tables may be built and used separately. Additionally oralternatively, the tables may be combined into fewer tables. In theseand other embodiments, each n-gram entry may include an indication thatindicates whether the n-gram entry is a regular, filtered, or faken-gram. Alternatively or additionally, the n-grams may all be put in onetable with no indications regarding the type of the n-gram so thatinformation regarding the method and date of creation of each n-gram isdeleted.

In the various methods described above for creating n-grams fromfiltered n-grams as well as other, after creation of an n-gram and anassociated n-gram counter, the n-gram counter may be incremented as then-gram is observed in transcriptions as discussed with respect to themethod 6700. Additionally and alternatively, n-grams may be created butnot counted. In some embodiments, fake n-grams may be created andcounted or not counted. Additionally or alternatively, fake n-grams maybe created at various times and using various language models. In someembodiments, after a language model (or n-gram table) is used to createa fake n-gram or to update its counter or probability, that version ofthe language model may be deleted. For example, a first language modelmay be used to create an n-gram and may then be deleted, a secondlanguage model may be used to update the n-gram counter or probabilityand may then be deleted, and so on. After n-grams (either in counterform or probability form) are created and/or counted or updated, then-grams may be used to train language models for use by ASR systems.

In counting and using n-grams and language models and in performingother computational operations, various methods exist and arecontemplated for determining and using values of parameters such ascounters, probabilities, logs, and products, among other parameters. Inthese and other embodiments, methods that result in comparable values orthat achieve similar results as those discussed above and in thisdisclosure may be considered as equivalent of the methods disclosed andas within the scope of the disclosure. For example, n-grams may beestimated, stored, and used as counts (e.g., the number of n-gramsobserved), probabilities (e.g., the probability that an n-gram occurs),log probabilities, conditional probabilities (the probability of a wordgiven its context), etc., and the associated forms and methods for usingthis different representations of n-grams or n-gram counts may beconsidered equivalent. As another example, a conditional log probabilitymay be expressed as log P(A|B), log P(AB)/P(B), or log P(AB)−log P(B).In these and other embodiments, the different expressions of theconditional log probabilities may be used with a similar result and maybe considered equivalent. Also, a language model may take any of severalforms, including an n-gram table, n-grams converted to probabilities,log probabilities, or conditional probabilities, exponential languagemodels, and language models with additional features such as smoothing,interpolation, and backoff probabilities, among other language modelforms, all of which may be considered equivalent and contemplated typesof language models.

The above steps may be generalized to other language model constructionmethods, including methods for training language models that use backoffprobabilities, and to other n-gram lengths (i.e., lengths other than n=3or n=4). The steps may be used to train other types of models such asacoustic models, confidence models, capitalization models, punctuationmodels, pronunciation models, feature extraction or transformationmodels, or other types of models.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations and actions, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

FIG. 64 is a flowchart of an example method 6800 of filtering n-gramsfor privacy, in accordance with some embodiments of the presentdisclosure. The method 6800 may be arranged in accordance with at leastone embodiment described in the present disclosure. The method 6800 maybe performed, in some embodiments, by processing logic that may includehardware (circuitry, dedicated logic, etc.), software (such as is run ona general-purpose computer system or a dedicated machine), or acombination of both. In some embodiments, the method may be performed bythe privacy filter 6004 of FIG. 56, or the consent detector 6202 of FIG.58. In these and other embodiments, the method 6800 may be performedbased on the execution of instructions stored on one or morenon-transitory computer-readable media. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, or eliminated, depending on the desiredimplementation.

The method 6800 may begin at block 6802 where one or more transcribedwords may be received and an n-gram may be extracted from the receivedtranscription. The method 6800 may simultaneously branch into twoprocesses at blocks 6804 and 6806. Although the branches may beperformed simultaneously, the branch that begins with block 6804 isdiscussed first.

At block 6804, it may be determined if the extracted n-gram exists inthe n-gram table. In response to the n-gram existing in the n-gramtable, at block 6806, the corresponding n-gram counter may beincremented. At block 6808, the incremented count may be compared to athreshold q. In response to the n-gram count not being greater than q,the n-gram may be maintained in the n-gram table but may not be used totrain the language model. In some embodiments, the variable q may be aminimum occurrence threshold and may depend on n (the length of then-gram) and other factors. In response to the counter being greater thanq, at block 6810, the n-gram and the associated counter may be providedto a language model trainer where the n-gram may be used to train thelanguage model.

Returning to block 6804, if is determined that the extracted n-gram doesnot exist in the n-gram table, at block 6812 it may be determined if theextracted n-gram includes a first and last name. In response to theextracted n-gram not including a first and last name, at block 6814, theextracted n-gram may be created and stored in the n-gram table. In someembodiments, the step of creating an n-gram table entry may includecreating or incrementing n-gram entries of length n, n−1, n−2, n−3,etc., with the associated counters of any new n-grams set to one or someother number.

In response to the extracted n-gram including a first and last name, themethod 6800 may proceed to block 6816. At block 6816, a filtered entryfor the n-gram may be created in the n-gram table. The first and lastname may be filtered by replacing the first name with “_FIRST_” and thelast name with “_LAST_.” Although the depicted embodiment illustratessteps for filtering a first and last name, any of the methods fordetecting and filtering sensitive information described above may beimplemented at blocks 6812 and 6816. Alternatively, the n-gram may bedeleted without updating the table.

Returning to block 6806, it may be determined whether a first party(“P1”) has provided consent to be recorded. If the determination is “no”then no recording is made. At block 6818 it may be determined if consenthas been received from the transcription party (“P2”), and whetherconsent is necessary (i.e., if P1 and P2 are from one-partystates/countries, consent of P2 may not be required). In response toconsent being necessary and not provided, no recording may be made. Inresponse to consent not being necessary or consent being necessary andbeing provided, the method 6800 may proceed to block 6820.

At block 6820, it may be determined if the communication session data(e.g., audio, transcription, n-gram, etc.) include sensitiveinformation, such as private or personal information. In response to thecommunication session data including sensitive information, the method6800 may proceed to block 6822. In response to the communication sessiondata not including sensitive information, the method 6800 may proceed toblock 6824.

At block 6822 the sensitive data may be deleted or filtered. Thesensitive data may include n-grams, audio, text, logs, etc. Data to berecorded may depend on the type of consent obtained by the relevantparty. Text may be filtered by deleting at least some of the sensitiveportions of the text. Audio may be filtered by identifying sensitiveportions of the text, using an ASR to align the text with the audio, anddeleting the segments of audio aligned with the sensitive portions ofthe text. In some embodiments, the processing logic redacts or deletesthe n-gram in a manner similar to that described above with reference toFIG. 63.

At block 6824, at least a portion of the communication session data isrecorded. In some embodiments, the communication session data may beperiodically purged or filtered. For example, an analysis of n-grams inthe n-gram table may be performed to identify age and size periodically,randomly, or in response to an event. For example, if the age of ann-gram and its associated counter meet a set of selected criteria, thenthe n-gram may be deleted or filtered. For example, if an n-gram is overX weeks old and the counter is less than Y, then the n-gram may bedeleted or filtered.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations and actions, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

FIG. 65 illustrates an example environment 6900 for distributedcollection of n-grams, in accordance with some embodiments of thepresent disclosure. In some embodiments, the environment 6900 mayinclude multiple transcription units 6914 a-d, collectively thetranscription units 6914. The transcription units 6914 may be configuredin various forms as described in this disclosure and configured togenerate transcriptions. For example, the transcription unit 6914 a mayinclude a CA client, multiple ASR systems including a speaker-dependentand one or more speaker-independent ASR systems, a text editor, languagemodels, and a fuser.

The transcription unit 6914 a may provide a transcription todenormalizer 6906. The denormalizer 6906 may denormalize thetranscription and provide the denormalized transcription to a privacyfilter 6910. The privacy filter 6910 may be configured to removesensitive information. The privacy filter 6910 may provide thetranscription to an n-gram counter 6908 configured to extract n-gramsfrom the transcription and count the number of occurrences for eachn-gram. Additionally or alternatively, the n-gram counter 6908 may alsoperform other n-gram processing functions shown in FIG. 63 such asn-gram table lookups, and creating new n-grams. The n-gram counter 6908may count occurrences of n-grams extracted from the transcriptions overa specified period of time to create a local n-gram table and upload thelocal n-gram table to an n-gram accumulator 6902. The upload may occurcontinuously, periodically, randomly, in response to some event, orduring off-hours, such as at night. After the upload, the n-gram counter6908 may reset counters in the local n-gram table to zero.

In some embodiments, the denormalizer 6906, the privacy filter 6910, andn-gram counter 6908 may be part of the transcription unit 6914 a.Alternatively or additionally, the denormalizer 6906, the privacy filter6910, and n-gram counter 6908 may be part of another system.

The transcription units 6914 b-d may be configured to generatetranscriptions. N-grams from the transcriptions may be exacted and thenumber of occurrences for each n-gram may be counted to form localn-gram tables. The local n-gram tables may also be uploaded to then-gram accumulator 6902. In these and other embodiments, thetranscription units 6914 b-d may include elements analogous to thedenormalizer 6906, the privacy filter 6910, and n-gram counter 6908.Alternatively or additionally, the transcription units 6914 b-d mayshare elements analogous to the denormalizer 6906, the privacy filter6910, and n-gram counter 6908.

The n-gram accumulator 6902 may be part of a central n-gram server 6904and may be configured to collect and total n-grams, n-gram counts,timestamps, and other fields in n-gram records from multipletranscription units 6914. The steps of accumulating n-grams may include,for each n-gram received from a local n-gram table:

-   -   1. The n-gram accumulator 6902 may look up the n-gram in a        master n-gram table.    -   2. If an n-gram in a local n-gram table is not found in the        master n-gram table, the n-gram accumulator 6902 may create the        n-gram in the master n-gram table with a counter value of zero.    -   3. The n-gram accumulator 6902 may add the n-gram counter value        in the local n-gram table to the corresponding n-gram counter in        the master n-gram table.

In some embodiments, a second privacy filter 6930 may be implemented inthe central n-gram server 6904 and configured to provide privacy inaddition to or instead of that offered by the privacy filter 6910. Thesecond privacy filter 6930 may be applied to n-grams either before orafter the master n-gram table is updated. A language model trainer 6932may train a language model and transmit the language model to one ormore ASR systems and/or fusers in the transcription units 6914.Additionally or alternatively, the transcription units 6914 may transmittranscriptions to the central n-gram server 6904, which may include adenormalizer, a privacy filter, and local n-gram counters analogous tothe denormalizer 6906, the privacy filter 6910, and n-gram counter 6908.

Additionally or alternatively, the transcription units 6914 may streamaudio to the central n-gram server 6904 that may perform speechrecognition, then extract, count, and create n-grams. One or more ASRsystems on the central n-gram server 6904 may return a transcription tothe respective transcription units 6914. Modifications, additions, oromissions may be made to the environment 6900 without departing from thescope of the present disclosure.

FIG. 66 is a flowchart of an example method 7000 of n-gram training, inaccordance with some embodiments of the present disclosure. The method7000 may be arranged in accordance with at least one embodimentdescribed in the present disclosure. The method 7000 may be performed,in some embodiments, by processing logic that may include hardware(circuitry, dedicated logic, etc.), software (such as is run on ageneral-purpose computer system or a dedicated machine), or acombination of both. In some embodiments, the method may be performed bythe n-gram counter 6508 and/or the central n-gram server 6904 of FIGS.61 and 65. In these and other embodiments, the method 7000 may beperformed based on the execution of instructions stored on one or morenon-transitory computer-readable media. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, or eliminated, depending on the desiredimplementation.

The method 7000 may begin at block 7002, where first audio data of acommunication session between a first device of a first user and asecond device of a second user may be obtained. The communicationsession may be configured for verbal communication. At block 7004,during the communication session, a text string that is a transcriptionof the first audio data may be obtained from an automatic transcriptionsystem.

At block 7006, during the communication session, a contiguous sequenceof words may be selected from the text string as a first word sequence.In some embodiments, the text string may be denormalized beforeselecting the contiguous sequence of words as the first word sequence.

At block 7008, during the communication session, the first word sequencemay be compared to multiple word sequences obtained before thecommunication session. In these and other embodiments, each of themultiple word sequences may be associated with a corresponding one ofmultiple counters.

In response to the first word sequence corresponding to one of themultiple word sequences based on the comparison, the method 7000 mayproceed to block 7010. In response to the first word sequence notcorresponding to one of the multiple word sequences based on thecomparison, the method 7000 may proceed to block 7016.

At block 7010, during the communication session, a counter of themultiple counters associated with the one of the multiple word sequencesmay be incremented. In some embodiments, each one of the multiplecounters may indicate a number of occurrences that a corresponding oneof the multiple words sequences is included in multiple transcriptionsof multiple communication sessions that occur between multiple devices.In these and other embodiments, the multiple devices may not include thefirst device and the second device.

At block 7012, after incrementing the counter of the multiple counters,the text string and the first word sequence may be deleted. In these andother embodiments, the first word sequence may be deleted during thecommunication session. Alternatively or additionally, the text stringmay be deleted during the communication session.

At block 7014, after deleting the text string and the first wordsequence, a language model of the automatic transcription system may betrained using the multiple word sequences and the multiple counters. Atblock 7016, the first word sequence may be added to the multiple wordsequences. At block 7018, a counter for the first word sequence may becreated and updated to a value of one.

Modifications, additions, or omissions may be made to the operationsdescribed above without departing from the scope of the presentdisclosure. For example, the operations may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations areonly provided as examples, and some of the operations may be optional,combined into fewer operations and actions, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

For example, the method 7000 may further include selecting a secondcontiguous sequence of words from the text string as a second wordsequence and comparing the second word sequence to the multiple wordsequences. The method 7000 may further include in response to the secondword sequence not corresponding to any of the multiple word sequencesbased on the comparison, adding a third word sequence based on thesecond word sequence to the multiple word sequences and adding a secondcounter with a count of one to the multiple counters that is associatedwith the third word sequence of the multiple word sequences. In theseand other embodiments, training the language model of the automatictranscription system using the multiple word sequences and the multiplecounters may occur after adding the second word sequence to the multipleword sequences.

In some embodiments, the third word sequence may be the same as thesecond word sequence, the third word sequence may include fewer wordsthan the second word sequence, or the third word sequence may include areplacement word that is a generic word of one of the words in thesecond word sequence. In these and other embodiments, the replacementword may be used in place of the one of the words in the second wordsequence such that the third word sequence and the second word sequencemay include a same number of words.

In some embodiments, the one of the words in the second word sequencemay be replaced based on the one of the words meeting a sensitivecriteria. In these and other embodiments, removal words removed from thesecond word sequence to generate the third word sequence that includesfewer words than the second word sequence may be removed based on theremoval words meeting the sensitive criteria.

In some embodiments, the method 7000 may further include adding the oneof the words in the second word sequence that is replaced by thereplacement word to the multiple word sequences and adding a thirdcounter with a count of one to the multiple counters that is associatedwith the one of the words in the second word sequence.

FIG. 67 illustrates an example environment 7100 for neural net languagemodel training, in accordance with some embodiments of the presentdisclosure. In general, the environment 7100 may be configured fortraining language models on-the-fly (i.e., without recording) bytraining a neural net language model 7102 as transcriptions aregenerated and before the transcriptions are deleted. An example of aneural net language model 7102 is an RNNLM (recurrent neural networklanguage model).

In some embodiments, a transcription unit 7114 may generate atranscription based on audio and send the transcription to thedenormalizer 7130. The denormalizer 7130 may denormalize thetranscription and forward the denormalized transcription to a neural nettrainer 7104. In some embodiments, the neural net trainer 7104 may usethe denormalized transcription to train the neural net language model7102 using gradient descent, back propagation, or another method fortraining neural nets. Once training from a transcription is complete,the transcription may be deleted. The neural net trainer 7104 may thencontinue with a second transcription. In some embodiments, training fora transcription may be considered complete when a selected number oftraining iterations have been performed. The number of trainingiterations may be one. Additionally or alternatively, training may beconsidered complete when the communication session producing content forthe transcription ends, such as when one or both parties disconnect orwhen transcription delivery to user devices ends. Additionally oralternatively, training may be considered complete when training on asecond transcription begins. The neural net trainer 7104 may provide theneural net language model 7102 to the transcription unit 7114 for use inthe ASR system, fuser, rescorer, or other element.

In some embodiments, the neural net trainer 7104 may be configured totrain on a pool of training data including transcriptions from multipleongoing communication sessions from multiple transcription units. Forexample, a first pool of training data may include a neural net trainingmini-batch. The neural net trainer 7104 may run training iterations onpart or all of the first pool of training data as long as the ongoingcommunication sessions continue. After one or more of the ongoingcommunication sessions end, the transcription for that communicationsession is removed from the first pool of training data and is no longerused by the neural net trainer 7104. After a new communication sessionbegins, a transcription for the new communication session is added to afirst pool or to a second pool of training data. The end of acommunication session may be variously defined as the point where oneparty disconnects, multiple parties disconnect, a particular partydisconnects, the transcription service stops sending transcriptions to auser device, a selected amount of time after one or more partiesdisconnect, or an ongoing training iteration using data from thecommunication session is complete, among others. In these and otherembodiments, the neural net language model 7102 may be provided to thetranscription units for use in ASR systems, fusers, and rescorers, amongother elements.

In some embodiments, after a neural net language model 7102 has beenconstructed, a synthetic text generator 7106 may use the neural netlanguage model 7102 in a generative mode to create n-grams or synthetictranscriptions. In these and other embodiments, synthetic transcriptionsmay include pseudo-random strings of words where the frequencies of wordstrings are based on word combination probabilities defined by theneural net language model 7102. The synthetic text may be stored in adatabase 7108. The synthetic text may be used to train a second languagemodel such as an n-gram based language model by a language model trainer7112.

In some embodiments, the second language model may be improved bycombining the second language model with other language models 7124 withan interpolator 7110 to create an updated language model. Examples ofother language models may include the neural net language model 7102, alanguage model built based on a first party's voice or content, alanguage model based on a second party's voice or content, a languagemodel based on communication session data from a specific account typesuch as business communication sessions, a commercially availablelanguage model or language model built from commercially available data,a language model built from a prior transcription service or fromanother service, a language model built from data from a group ofcommunication sessions where the group is defined using a clusteringmethod, a language model built from text sources, or a generic languagemodel built from multiple sources of text (see also FIGS. 82 and 83). Inthese and other embodiments, the generation of the updated languagemodel may be performed on-the-fly or after a communication session hasended.

In some embodiments, the environment 7100 may be configured to provide aset of multiple language models such as the neural net language model7102, the second language model, and other language models to thetranscription unit 7114. The elements in the transcription unit 7114 mayuse multiple language models in place of a single language model, forexample, by determining a conditional probability based on a weightedsum of the conditional probabilities determined by each of the multiplelanguage models.

In some embodiments, the updated language model and/or the neural netlanguage model 7102 may be used by an ASR system 7120 in thetranscription unit 7114 to generate a transcription. Additionally oralternatively, the neural net language model 7102 may be used by the ASRsystem 7120 to generate a transcription and the second language modeland/or updated language model may be used by the ASR system 7120 togenerate multiple transcription hypotheses. The multiple transcriptionhypotheses may be in the form of, for example, an n-best list, WCN, orlattice. In these and other embodiments, the environment 7100 may usethe neural net language model 7102, via a rescorer 7140, to select thebest hypothesis among the possibilities provided by the ASR system 7120and to use this best hypothesis as the transcription.

Modifications, additions, or omissions may be made to the environment7100 without departing from the scope of the present disclosure. Forexample, although FIG. 67 is illustrated using a neural net languagemodel 7102 and a neural net trainer 7104, it is to be understood thatother forms of language models and language model training may also beused. Also, alternative forms of neural nets such as feed-forward neuralnets, LSTMs, CNNs, and other topologies may be used for neural netlanguage models.

FIG. 68 illustrates an example environment 7200 for distributed modeltraining, in accordance with some embodiments of the present disclosure.In some embodiments, transcription units 7214 a-f, collectivelytranscription units 7214, may be configured to generate transcriptionsfrom audio and provide the transcriptions and the audio to a modeltrainer 7206. In particular, the transcription units 7214 a-f may eachprovide the transcriptions and the audio to one of multiple trainerdevices 7202 a-c, collectively trainer devices 7202. Each trainer device7202 may be configured to adapt a model (e.g., language model, acousticmodel, etc.) based on data from the transcription units 7214 andtransmit the adapted model to an accumulator 7204. The model trainer7206 may include one or more CPUs or vector processors such as GPUs(graphical processing units) or other SIMD (single instruction multipledata) processors that may be used to perform the functionality of theelements of the model trainer 7206 discussed with respect to environment7200.

In some embodiments, the accumulator 7204 may use adapted models togenerate a set of parameter updates which may be used to update a mastermodel 7208. For example, the accumulator 7204 may determine an updatedmodel parameter by averaging across values of the parameter in modelsreceived from the multiple trainer devices 7202. The master model 7208may be transmitted back to the trainer devices 7202 for additionaliterations. The master model 7208 may be transmitted to thetranscription units 7214 for use by the transcription units 7214. Anexample of the operations executed by the model trainer 7206 mayinclude:

-   -   1. Master model parameters are set to an initial state. The        values of the initial state may be values determined from a        previous model training process or the values of the initial        state may be random numbers.    -   2. The master model 7208 may be distributed to multiple trainer        devices 7202.    -   3. A block of data may be collected from one or more        transcription units 7214 and distributed across one or more        trainer devices 7202.    -   4. Each trainer device 7202 may generate a model update based on        data from one or more transcription units 7214. The model update        may be used to create an updated model.    -   5. Each trainer device 7202 may transmit its respective model        update or updated model to the accumulator 7204. Alternatively        or additionally, each trainer device 7202 may send a set of        transformed parameters representing the model update or updated        model to the accumulator 7204.    -   6. The accumulator 7204 may combine model updates or updated        models from the trainer devices 7202 to generate a set of        parameters for updating the master model 7208. For example, if        the vector x represents a set of parameters in the master model        7208 and vectors x1, x2, and x3 represent a set of corresponding        parameters in updated models received from the training devices,        then the updated set of parameters for the master model 7208 may        be (x1+x2+x3)/3. Additionally or alternatively, the parameters        may by updated gradually by specifying a learning rate u, which        may be a number between 0 and 1, such that the updated parameter        set may be (1−u)*x+u*(x1+x2+x3)/3.    -   7. The parameters may be used to update the master model 7208.    -   8. Steps 2-7 may be repeated for each new block of data.

As discussed above, the operations describe that the accumulator 7204may combine model updates or updated models. In this disclosure, theterms model updates and updated models may be interchangeable.Alternatively or additionally, a method creating or using one of theterms model updates and updated models may be synonymous with creatingor using the other of the terms model updates and updated models.Generally, updated models may be described as models where parameters orother model features have been updated. Model updates may be describedas a set of information, such as a set of features values, adjustments,or values to be used to modify a model, for updating the model. Based onthese descriptions, an updated model may simply replace a previous modelwhere a model update may be a set of instructions for updating a model.In either case, the end result is an updated model, however the resultmay be achieved with slightly different processing. Thus, embodimentsthat discuss a model update may also be performed using an updated modelor vice versa without departing from the scope of the present disclosureor without detracting from the essence of the disclosed embodiments.

Additionally, the operations described above may be used to trainvarious types of models such as language models, acoustic models, andneural network implementations of models. The trainer devices 7202 andthe accumulator 7204 are illustrated as residing in a central location,namely the model trainer 7206; however, the trainer devices 7202 and theaccumulator 7204 may run in other locations such as on the transcriptionunits 7214.

Furthermore, modifications, additions, or omissions may be made to theoperations described above without departing from the scope of thepresent disclosure. For example, the operations may be implemented indiffering order. Additionally or alternatively, two or more operationsmay be performed at the same time. Furthermore, the outlined operationsare only provided as examples, and some of the operations may beoptional, combined into fewer operations, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

In some embodiments, the environment 7200 may train the master model7208 and/or one or more of the models in the trainer devices 7202on-the-fly. In these and other embodiments, the models may be updatedwith communication session data as the communication session data iscreated and before the communication session data is deleted. Thedeletion of the communication session data may occur during thecommunication session, at the end of the communication session, orshortly after the end of the communication session and may be before anew set of data is received and used to train the model. Additionally oralternatively, the communication session data may be stored and thetraining of the models may occur at any time.

Modifications, additions, or omissions may be made to the environment7200 without departing from the scope of the present disclosure. Forexample, in some embodiments, the model trainer 7206 may include a datacollector. The data collector may collect the data, for exampletranscriptions and audio from the transcription units 7214. Thecollected data may then be distributed to the individual trainer devices7202.

FIG. 69 illustrates an example environment 7400 for a centralized speechrecognition and model training, in accordance with some embodiments ofthe present disclosure. In some embodiments, the environment 7400 mayinclude a central speech manager 7406 that may include an ASR API 7404,an ASR cluster 7402 of multiple ASR systems, and a model trainer 7430,which may be analogous to the model trainer 7206 of FIG. 68. Theenvironment 7400 may also include a transcription unit 7414 that mayoutput a transcription based on a fusion of multiple transcriptions. Inthese and other embodiments, the transcription unit 7414 may include anaudio interface 7740 configured to receive audio and output revoicedaudio and a text editor 7426 configured to obtain edits to edittranscriptions. The transcription unit 7414 may further include arevoiced ASR system 7420 that receives the revoiced audio and that isconfigured based on a CA profile 7422, a first ASR call module 7450 thatreceives the revoiced audio, and a second ASR call module 7452 thatreceives the audio. Each of the ASR system 7420, the first ASR callmodule 7450, and the second ASR call module 7452 may output atranscription that may be fused by a fuser 7424 configured based on alanguage model 7411. The output transcription may be edited by the texteditor 7426.

In some embodiments, the first ASR call module 7450 and the second ASRcall module 7452 may each be configured with interfaces to one or moreremote ASR APIs 7404. For example, the first ASR call module 7450 mayforward audio to the ASR API 7404, which in turn may pass the audio tothe ASR cluster 7402. The ASR cluster 7402 may generate a transcriptionand return the transcription to the ASR API 7404. The ASR API 7404 maybe configured to return the transcription to the first ASR call module7450 where the transcription may be used by the transcription unit 7414as described above.

In some embodiments, the ASR API 7404 (and, depending on theconfiguration, ASR systems in the ASR cluster 7402) may forward datafrom the transcription unit 7414 and from the ASR cluster 7402 to themodel trainer 7430. The model trainer 7430 may use the data to create orupdate one or more ASR models. The environment 7400 may be configured totrain models on-the-fly or based on stored data. As described above, theoutput of the transcription unit 7414 may be enhanced with punctuationand capitalization using a module 7462 and may be sent to a user deviceas a transcription.

An example implementation of the steps executed by the environment 7400may include:

-   -   1. Audio is sent to first ASR call module 7450.    -   2. Revoiced audio is sent to second ASR call module 7452.    -   3. First ASR call module 7450 forwards the audio to the ASR API        7404.    -   4. Second ASR call module 7452 forwards the revoiced audio to        the ASR API 7404.    -   5. The ASR API 7404 forwards the audio and the revoiced audio to        ASR systems in the ASR cluster 7402.    -   6. The ASR cluster 7402 returns transcriptions via the ASR API        7404 to first ASR call module 7450 and second ASR call module        7452, respectively.    -   7. The transcription unit 7414 may generate a transcription and        may forward the transcription to the ASR API 7404. The        transcription may be created using one or more of: a        transcription from ASR system 7420 using the revoiced audio, a        transcription from first ASR call module 7450, a transcription        from second ASR call module 7452, a transcription from the fuser        7424, and text edits by a CA from a text editor 7426.    -   8. Capitalization and Punctuation is added to the transcription        and sent to the user device.    -   9. The ASR API 7404 and ASR cluster 7402 may forward the audio,        revoiced audio, and the transcription from the transcription        unit 7414 to the model trainer 7430.    -   10. The model trainer 7430 may use the audio, the revoiced        audio, and the transcription to update ASR models.    -   11. The transcription unit 7414 and central speech manager 7406        may delete the audio, the revoiced audio, and transcription at a        selected time. The selected time may be, for example, at the end        of the communication session, once training on the audio and        transcription described above is complete, before a second        training iteration begins, or once communication session        transcriptions are complete.

In some embodiments, the model trainer 7430 may use the CA profile 7422or a caller profile 7410 of one or more callers of a communicationsession that generates the audio, such as the transcription party, fortraining a model for use by multiple speakers. In these and otherembodiments, the caller profile 7410 may contain a model and/orinformation used to train a model. The ASR system 7420 may adapt to aCA's voice and may save training or adaptation information in the CAprofile 7422. Likewise when an ASR system, such as the first ASR callmodule 7450, adapts to a caller's voice, such as to a particulartranscription party, the ASR system may save training information in thecaller profile 7410. The training information may be saved in the CAprofile 7422 and the caller profile 7410 may be part of aspeaker-dependent model or it may include information to be used fortraining or adapting a speaker-independent ASR model, among other typesof ASR models.

In some embodiments, a profile manager 7460 may save and distribute theCA profiles 7422 and/or caller profiles 7410. For example, when a CAlogs onto a CA workstation, the CAs profile 7422 may be downloaded fromthe profile manager 7460 and used by the ASR system 7420 to generatetranscriptions of the revoiced audio. As another example, whencommunication session audio is transmitted to an ASR system, a callermay be identified, such as by the caller's device identifier, and theprofile corresponding to the caller may be downloaded from the profilemanager 7460. The profile of the caller may be provided to the ASRsystem to be used to generate transcriptions based on audio thatincludes the caller's voice. In some embodiments, the profile manager7460 may upload one or more caller profiles 7410 or CA profiles 7422 tothe model trainer 7430. The profile manager 7460 may select a subset ofone or more CA profiles 7422 based on performance, accuracy, skills,experience, location, or other characteristics of the associated CA tobe sent to the model trainer 7430. The model trainer 7430 may use theone or more profiles or the subset to train ASR models, which may thenbe used to transcribe audio for one or more parties. In someembodiments, the model trainer 7430 may use language model parameters orstatistics extracted from at least the subset of CA profiles 7422 totrain a new language model. For example, the model trainer 7430 mayextract probabilities or n-grams from multiple CA profiles 7422, averageor total the probabilities or n-grams, and convert the averages ortotals into a language model. The language model may be used to updatemultiple CA profiles 7422. Additionally or alternatively, theprobabilities, n-grams, or new language model may be used to generatetext data and the ASR system 7420 may train or adapt a language modelbased on the generated text data. An example of using profiles to trainASR models may be implemented as follows:

-   -   1. A first CA listens to communication session audio from a        first communication session and revoices communication session        audio by providing first revoiced audio to the ASR system 7420.        The communication session audio may include a voice sample from        the transcription party.    -   2. The ASR system 7420 transcribes the first revoiced audio to        provide transcriptions to a subscriber.    -   3. Information from the first communication session may be saved        in a first CA profile.        -   a. The information may include text and/or first revoiced            audio from the first communication session.        -   b. The information may include data extracted from text            and/or first revoiced audio from the first communication            session.        -   c. The information may include at least part of an ASR model            used by the ASR system 7420.        -   d. The information may include features extracted from an            ASR model used by the ASR system 7420.    -   4. An adapter 7412 may use the first CA profile to adapt a        speech model to the first revoiced audio creating a first        adapted speech model.    -   5. The adapter 7412 may use a second CA profile (obtained from        second revoiced audio of a second CA on a second communication        session in a manner similar to that of the first CA profile) to        adapt a speech model to the second CA, creating a second adapted        speech model.    -   6. The first CA may revoice communication session audio that is        provided to the second ASR call module 7452. The second ASR call        module 7452 may use the first adapted speech model to generate a        transcription.    -   7. The second CA may revoice communication session audio into        the second ASR call module 7452. The second ASR call module 7452        may use the second adapted speech model to generate a        transcription.    -   8. The model trainer 7206 may use the first and second CA        profiles to create or adapt a third ASR model.    -   9. The third ASR model may be transmitted to the transcription        unit 7414 and other transcription units.    -   10. Both the transcription unit 7414 and other transcription        units may use the third ASR model to transcribe communication        session audio or revoiced audio.

In some embodiments, the profile manager 7460 may upload one or morecaller profiles 7410 and provide the profiles to the model trainer 7430.Each caller profile 7410 may, for example, be a profile adapted to aspecific transcription party voice. The caller profile 7410 may be usedto train a speaker-dependent ASR system for use with the caller's voice.Alternatively or additionally, the caller profile 7410 together withother caller profiles may be used to train a speaker-independent ASRsystem adapted to multiple voices and used to recognize multiple voices.An example follows:

-   -   1. An ASR system transcribes audio from a first caller's voice        sample on a first communication session to generate a        transcription.    -   2. Information from the first communication session may be saved        in a first caller profile.        -   a. The information may include text and/or caller audio from            the first communication session.        -   b. The information may include data extracted from text            and/or caller audio from the first communication session.        -   c. The information may include at least part of an ASR            model.        -   d. The information may include features extracted from an            ASR model.    -   3. The adapter 7412 may use the first caller profile to adapt a        speech model to the first caller's voice, creating a first        adapted speech model.    -   4. The adapter 7412 may use a second caller profile (obtained        from a second caller's voice sample on a second communication        session in a manner similar to that of the first caller profile)        to adapt a speech model to the second caller's voice, creating a        second adapted speech model.    -   5. An ASR system may use the first adapted speech model to        transcribe audio from the first caller to create a        transcription.    -   6. An ASR system may use the second adapted speech model to        transcribe audio from the second caller to create a        transcription    -   7. The model trainer 7206 may use the first and second caller        profiles to create or adapt a third ASR model.    -   8. The third ASR model may be transmitted to the transcription        unit 7414 and other transcription units.    -   9. Both the transcription unit 7414 and other transcription        units may use the third ASR model to transcribe communication        session audio or revoiced audio. In some embodiments, the        communication session audio may be from a third caller who is        not the first or second caller.

Modifications, additions, or omissions may be made to the environment7400 without departing from the scope of the present disclosure.Additionally or alternatively, one or more of the first ASR call module7450 and the second ASR call module 7452 may be replaced with a regularASR system.

FIG. 70 illustrates an example environment 7500 for training models fromfused transcriptions, in accordance with embodiments of the presentdisclosure. In some embodiments, audio may be provided to a processingcenter 7501. The audio may be from a communications session. Theprocessing center 7501 may include an automatic communication sessiondistributor (ACD) 7530, multiple transcription units 7514 a-e,collectively transcription units 7514, and a fuser 7524. The ACD 7530may transmit the audio signal to one or more of the transcription units7514. The transcription units 7514 may generate transcriptions based onthe audio. The transcriptions may be provided to the fuser 7424. Thefuser 7524 may combine the transcriptions into a fused transcription. Insome embodiments, the fused transcription may be provided to a userdevice.

In some embodiments, the fused transcriptions may also be provided to amodel trainer 7522. The model trainer 7522 may use the fusedtranscription to train or adapt one or more models 7504. For trainingacoustic models, the ACD 7530 may also transmit the audio signal to themodel trainer 7522. When recording of the audio is allowed, the modeltrainer 7522 may train the models 7504 from information from multiplestored communication sessions. When recording of the audio is notallowed, the model trainer 7522 may update the models 7504 on-the-fly,using, for example gradient descent or other iterative methods (see FIG.63), from each communication session information record before thecommunication session information record is deleted. A model manager7502 may be used to store and track the models 7504. In someembodiments, before transmitting an audio signal to multipletranscription units 7514, the ACD 7530 may first determine if there aresufficient available transcription units 7514. The ACD 7530 maydetermine if there are sufficient available transcription units 7514 bycomparing the current or projected traffic load to the availabletranscription units pool or by measuring the average transcription units7514 idle time between communication sessions (see also FIG. 47).Modifications, additions, or omissions may be made to the environment7500 without departing from the scope of the present disclosure.

FIG. 71 illustrates an example environment 7600 for training models ontranscriptions from multiple processing centers, in accordance with someembodiments of the present disclosure. The environment 7600 includesmultiple processing centers 7601 a-d, collectively processing centers7601. Each of the processing centers 7601 may be configured in a mannerto the configuration of the processing center 7501 of FIG. 70. Each ofthe processing centers 7601 may receive audio from one or more sources,such as one or more communication sessions. The processing centers 7601may be configured to generate data such as audio, transcriptions, andlog data for each audio source. In some embodiments, the data may beanalogous to communication session data discussed in this disclosure.The data may be forwarded to a model trainer 7606. Transmission of thedata to the model trainer 7606 may occur at various times, including:

-   -   1. As the data is created.    -   2. On a regular schedule.    -   3. When there is available bandwidth, such as at night or during        off-peak hours.    -   4. When the model trainer 7606 requests the data.    -   5. When the model trainer 7606 is available to train.    -   6. When storage space used to store communication session data        at the processing center is needed for other purposes.

The model trainer 7606 may distribute the data to a model updater 7602.The model updater 7602 may include CPUs, GPUs, or other vectorprocessors. The model trainer 7606 may also provide the master model7608, or a portion thereof, to the model updater 7602. The model updater7602 may adapt model parameters based on the data and a master model7608 and transmit updates back to the model trainer 7606 for updatingthe master model 7608.

In some embodiments, the model updater 7602 may include multipleupdaters. In these and other embodiments, the model updater 7602 mayprovide a portion of the data to each of the updaters. The model trainer7606 may also transmit at least part of the master model 7608 to eachupdater. In these and other embodiments, the updaters may each includemodel parameters based on the data and the master model 7608 andtransmit the updates back to the model trainer 7606. The model trainer7606 may use updates from the model updaters 7602 to update the mastermodel 7608.

In some embodiments, the model trainer 7606 may download multiplespeaker-dependent models, each trained to their respective CA or callingparty, from model updaters 7602. The model trainer 7606 may combine thespeaker-dependent models to create or update the master model 7608. Insome embodiments, parameters in the speaker-dependent models may beaveraged to generate corresponding parameters in the master model 7608.Additionally or alternatively, speech samples may each be presented tomultiple speaker-dependent models for transcription. Transcriptions fora given speech sample may be fused to create a high-accuracytranscription, which may be used to train the master model 7608.

After being adapted, the master model 7608 may be distributed to thetranscription units in the processing centers 7601 for use intranscription of communication sessions. The distribution may betriggered by completion of a new master model or by a request fromtranscription units. In these and other embodiments, a transcriptionunit may query the model trainer 7606 to determine if a model update isavailable. The response to the query may include the current and/or newmodel version number.

In some embodiments, only part of the master model 7608 may bedistributed to transcription units such as when only part of the mastermodel 7608 has been updated. In these and other embodiments, thetranscription units may use a previous copy of other parts of the mastermodel 7608 to generate transcriptions of audio. For example,transcription units may use an existing acoustic model together with anupdated language model received from the model trainer 7606.

In some embodiments, each transcription unit may transcribe audio from asingle communication session or multiple transcription units may worktogether to transcribe audio from a single communication session. Inthese and other embodiments, the transcriptions from multipletranscription units transcribing a communication session may be fusedtogether to create a higher-accuracy transcription. The higher-accuracytranscription may be provided to the model trainer 7606 for use inupdating or training the master model 7608.

Modifications, additions, or omissions may be made to the environment7600 without departing from the scope of the present disclosure. Forexample, in some embodiments, each of the processing centers 7601 mayinclude a model updater. The arrangement of each processing center 7601including a model updater may allow excess capacity computing systems inthe processing centers 7601 to be used for training models. In these andother embodiments, the model updater in each processing center 7601 maycommunicate with the model trainer 7606 to obtain the master model 7608or parts thereof or to send updates of the master model 7608 thereto.

FIG. 72 illustrates an example environment 7800 for distributed modeltraining, in accordance with some embodiments of the present disclosure.The environment 7800 illustrates multiple devices 7804 a-c, collectivelythe devices 7804. The devices 7804 may be communication devices that maybe used by users to establish a communication session. The devices 7804may include captioned phones, smart phones, tablets, computers, andmobile devices, among other devices. In some embodiments, a modelupdater 7802, such as a unit analogous to the model updater 7602 of FIG.71, may be configured to run on the device 7804 a and communicate with amodel trainer 7806, which may be analogous to the model trainer 7206 ofFIG. 68. The device 7804 a may communicate with the model trainer 7806with respect to master models 7808 a-c, collectively master models 7808.In these and other embodiments, each of the devices 7804 may alsoinclude a model updater that may communicate with the model trainer 7806with respect to the master models 7808.

In these and other embodiments, the environment 7800 illustrates anexample of distributed training of models for ASR systems. Variousembodiments of distributed training may be implemented in theenvironment 7800, including:

-   -   1. The model trainer 7806 may distribute one or more master        models 7808 or parts of master models 7808 to one or more model        updaters 7802. The model updater 7802 may use communication        session information collected during one or more communication        sessions to train a model for improved accuracy and create a        model update. The model updater 7802 may be configured to        conduct training during the following example times:        -   a. At least partly during the communication session.        -   b. At least partly after the communication session using            communication session information saved during the            communication session.        -   c. During off-hours, such as at night.        -   The model update may be transmitted to the model trainer            7806. The model trainer 7806 may use the model update to            update the master models 7808. The master models 7808 may be            distributed to multiple ASR systems of the devices 7804.    -   2. The model trainer 7806 may receive data, such as        communication session information or information derived from        communication session information, from a first model updater        7802 on the device 7804 a and transmit the data to a second        model updater on the second device 7804 b. The model updater on        the second device 7804 b may use the data to create a model        update. The training may happen:        -   a. During a communication session, if resources are            available.        -   b. When the communication device is not processing a            communication session.    -   c. After communication session information is received from the        model trainer and when the communication device has available        processing and memory resources.        -   d. During off-hours, such as at night.        -   The model update may be transmitted to the model trainer            7806. The model trainer 7806 may use the model update to            update the master models 7808. The master models 7808 may be            distributed to multiple devices 7804. Additionally or            alternatively, the master models 7808 may be distributed to            other locations running ASR systems such as an ASR cluster,            transcription units, CA workstations, etc.    -   3. In some embodiments, the model updater 7802 may run at least        partly on a coprocessor such as a SIMD, vector processor, or        GPU. The coprocessor may be part of or external to the        communication device.    -   4. The process for updating models may be split between a device        7804 and a processing device separate from the devices 7804.    -   5. One or more master models may be distributed to the devices        7804, where the master models are updated based on audio        received by and text generated at the devices 7804. The updated        master models are transmitted back to the model trainer 7806        where the updated master model may be combined with updated        master models from one or more devices 7804, for example by        averaging neural net weights, to create a new version of the        master models 7808. To save communication bandwidth, the model        trainer 7806 and model updaters 7802 may transmit the master        model in a compressed format such as sending only portions of        the master model that have changed, by quantizing the weights        for transmission, or by sending only the difference in weight        changes.

Modifications, additions, or omissions may be made to the environment7800 without departing from the scope of the present disclosure.

FIG. 73 illustrates an example environment 7900 for distributed modeltraining, in accordance with some embodiments of the present disclosure.In some embodiments, environment 7900 includes a model updater 7902 thatmay be configured to perform at least part of the processing to train orupdate ASR models. As illustrated, the model updater 7902 includes anadapter 7912, a local data store 7904, an ASR system 7920, and a storagelocation 7916 for storing adapted models generated by the adapter 7912.In these and other embodiments, the model updater 7902 may be includedin a device, such as illustrated in FIG. 72. Alternatively oradditionally, the model updater 7902 may be included on a server.

In some embodiments, the model updater 7902 may be configured tocommunicate with a model trainer 7906 that may include a master model7918. In these and other embodiments, the master model 7918 may bedistributed, in part or in whole, to model updater 7902 and other modelupdaters on other devices or servers for training. In some embodiments,the model updater 7902 and the other model updaters may send adaptedmodels to a model combiner 7908 of the model trainer 7906 that maycombine the adapted models to generate updated master models 7918.

In some embodiments, the model updater 7902 may generate an adaptedmodel based on local data. The local data may include data from a devicethat includes the model updater 7902. For example, the local data mayinclude communication session data such as audio, a transcription of theaudio, log information such as a speaker's identity and a phone number,or other device identifier data, among other data. In some embodiments,the communication session data may be from a current communicationsession. Additionally or alternatively, the local data may be fromprevious communication sessions. In some embodiments, the model updater7902 may generate an adapted model based on remote data. The remote datamay include previously stored communication session data orcommunication session data from other devices. In some embodiments, themodel updater 7902 may perform operations including:

-   -   1. The model updater 7902 receives, from the model trainer 7906,        all or part of the master model 7918, denoted as a pre-adapted        model.    -   2. The ASR system 7920 may transcribe audio, such as        communication session audio or revoiced audio to create an ASR        transcription.    -   3. The model updater 7902 may send the ASR transcription for        display.    -   4. The model updater 7902 may use local data, optionally remote        data, the ASR transcription, and the pre-adapted model to create        an adapted model. The local data and/or remote data may be        currently being received data or stored data from the local data        store 7904.    -   5. The adapted model may be stored in the storage location 7916        and may be used by the ASR system 7920 for generating        transcriptions of audio.    -   6. The model updater 7902 may transmit the adapted model to the        model combiner 7908.

Various other devices, such as the devices 7804 of FIG. 72 may alsogenerate adapted models and provide the adapted models to the modelcombiner 7908. The model combiner 7908 may be configured to combine theadapted models from the multiple model updaters to create an update forthe master models 7918. In these and other embodiments, the updatedmaster model 7918 may be transmitted to one or more ASR systemsassociated with the multiple model updaters, including the ASR system7920. The updated master model 7918 may be used to generatetranscriptions for regular or revoiced audio by the ASR systems.

In some embodiments, the model combiner 7908 may be configured toreceive multiple adapted models that each have substantially the sametopology. In these and other embodiments, the model combiner 7908 mayuse weights or parameters at a given location in each adapted model todetermine a weight or parameter at the same location in an updatedmaster model. The model combiner 7908 may perform a similar procedurefor other weights or parameters in other locations in the adaptedmodels.

For example, if a first adapted model includes a matrix of weights W1from a first model updater and a second adapted model includes a matrixof weights W2 from a second model updater, then the model combiner 7908may determine new master model weights using element-by-element additionof the two matrices. In matrix notation, for example, where W1 and W2are 2×2 matrices,

${{W\; 1} = \begin{pmatrix}{w\; 1\left( {1,1} \right)} & {w\; 1\left( {1,2} \right)} \\{w\; 1\left( {2,1} \right)} & {w\; 1\left( {2,2} \right)}\end{pmatrix}},{{W\; 2} = \begin{pmatrix}{w\; 2\left( {1,1} \right)} & {w\; 2\left( {1,2} \right)} \\{w\; 2\left( {2,1} \right)} & {w\; 2\left( {2,2} \right)}\end{pmatrix}},$

then the master model weight W may be determined as

$W = {{{W\; 1} + {W\; 2}} = {\begin{pmatrix}{{w\; 1\left( {1,1} \right)} + {w\; 2\left( {1,1} \right)}} & {{w\; 1\left( {1,2} \right)} + {w\; 2\left( {1,2} \right)}} \\{{w\; 1\left( {2,1} \right)} + {w\; 2\left( {2,1} \right)}} & {{w\; 1\left( {2,2} \right)} + {w\; 2\left( {2,2} \right)}}\end{pmatrix}.}}$

Models may include acoustic models, language models, neural networkweights, or end-to-end ASR models. The model training may occuron-the-fly or from stored data.

In some embodiments, at least part of the model updater 7902 may beincluded in or share resources, such as memory or processing, with anASR system, user device, server, transcription party device, or CAclient, among other devices. In this manner, the model updater 7902 mayuse processing resources not being used during communication sessions atparticular times, such as during or between communication sessions orduring off-peak hours. The process of transmitting the master models7918 to the model updater 7902 and transmitting adapted models from themodel updater 7902 to the model trainer 7906 may include one or moreforms of compression, for example:

-   -   1. The transmitted information may reflect the difference        between the current model and the previous model. For example,        if a previous master model Wp is a matrix of parameters and is        to be updated with a new master model Wn, then the difference        Wd=Wn−Wp may be transmitted. The master model 7918 may then be        updated using Wn=Wp+Wd.    -   2. The model may exist in multiple parts. At any given time,        some parts may be updated and some not. Non-updated model parts        may be stored at the model updater 7902 and model trainer 7906.        Model parts to be updated may be transmitted between the model        updater 7902 and model trainer 7906.

Modifications, additions, or omissions may be made to the environment7900 without departing from the scope of the present disclosure.

FIG. 74 illustrates an example environment 8000 for distributed modeltraining, in accordance with some embodiments of the present disclosure.The environment 8000 includes a model updater 8002 and a model trainer8016. The model updater 8002 and the model trainer 8016 may be analogousto the model updater 7902 and a model trainer 7906 of FIG. 73, exceptthe model updater 7902 and a model trainer 7906 may be configured totransmit compressed versions of the models and model updates. Thediscussion with respect to FIG. 74 focuses on the aspects of the modelupdater 8002 and the model trainer 8016 that provide for transmission ofcompressed versions of models. Aspects described previously in FIG. 73may not be repeated. For example, an ASR system 8020, an adapter 8012,an model database 8006, and master models 8018 may be analogous to theASR system 7920, the adapter 7912, the storage location 7916, and themaster models 7918 of FIG. 73 and may not be discussed further.

In some embodiments, the model trainer 8016 may transmit at least partof a master model 8018, designated here as a pre-adapted model, to themodel updater 8002. The pre-adapted model may be a compressed form of atleast part of the master model 8018. The model updater 8002 may adapt atleast part of the master model 8018 and generate an adapted model, whichthe model updater 8002 may return to the model trainer 8016. The modeltrainer 8016 may use the adapted model to adapt the master model 8018.In some embodiments, the model trainer 8016 may transmit a pre-adaptedmodel to multiple model updaters 8002, receive adapted models from themultiple model updaters, and use the adapted models to adapt one or moremaster models 8018.

In some embodiments, the model updater 8002 may compress or quantize theadapted model before sending the adapted model to the model trainer8016. The model updater 8002 may be configured to store any quantizationerror in the adapted model and include the error as an input to aquantizer 8014 next time an adapted model is generated and sent. In thismanner, quantization errors in a given adapted model may be corrected insubsequent updates. An example of operations for adapting and quantizingmodels for transmission may include:

-   -   1. The model trainer 8016 transmits a pre-adapted model to the        adapter 8012.    -   2. A data source such as information from a communication        session may be sent to the ASR system 8020 and to the adapter        8012.    -   3. The ASR system 8020 may transcribe a speech sample from audio        of the communication session and send the transcription to the        adapter 8012.    -   4. The transcription may also be sent to a user device for        display.    -   5. The adapter 8012 may use the data source and the        transcription to create an adapted model that may be stored in        the adapted model database 8006.    -   6. The adapted model may optionally be transmitted to the ASR        system 8020 for use in transcribing audio.    -   7. An adder1 8030 a may subtract the value of elements in the        pre-adapted model from the value of elements in the adapted        model to determine a difference signal. The step of subtracting        may include element-by-element matrix subtraction, which may be        similar to element-by-element addition described in FIG. 73. The        adder1 8030 a may also add a residual from a residual database        8004 to the difference signal.    -   8. The difference signal may be sent to the quantizer 8014,        which may quantize the signal Quantizing the signal may include        the quantizer 8014 mapping the difference signal to a countable        set of values. Each value may be represented by an index such as        a string of bits. Model quantization may be done in several        ways, including:        -   a. The quantizer 8014 may define multiple regions, each            associated with an index. If the value of a parameter falls            within a given region, the quantized parameter is set to an            index associated with that region. For example, the            quantizer 8014 may quantize a continuous variable into an            integer expressed using a selected number of bits. In a            quantizer 8014 that is linear, each region may be the same            size.        -   b. The quantizer 8014 may be one-bit that may establish a            threshold. If the value of a parameter is above the            threshold, the quantizer 8014 may set the quantized            parameter to one. If the value of a parameter is below the            threshold the quantizer 8014 may set the quantized parameter            to zero.        -   c. The quantizer 8014 that is a vector quantizer may compare            a parameter vector including one or more model parameters to            vectors in a codebook 8026. The codebook vector closest to            the parameter vector may be considered to be a match. Each            vector in the codebook 8026 may have an associated index.            The index of the matching codebook vector may be used as the            quantized parameter and may be sent in the model update to            the model trainer. A copy of the codebook 8026 may be held            by both the model updater 8002 and the model trainer 8016. A            decoder 8010 in the model trainer 8016 may decode the            parameter by retrieving the codebook entry corresponding to            the index.        -   d. A subset of parameters may be selected for quantization            and transmission. In various embodiments, parameters not            selected may be discarded, ignored, transmitted in an            uncompressed form, or included in the residual signal.    -   9. An adder2 8030 b may determine a quantization error, denoted        as the residual database 8004 signal, by subtracting the output        of the quantizer 8014 from the input to the quantizer 8014. The        residual may be saved to be used in a future update in the        residual database 8004. By saving the residual database 8004 and        later adding it to the difference signal, the model updater 8002        accounts for quantization error by including a correction for        quantization error in a subsequent update.    -   10. An encoder 8008 may receive the quantized signal and send it        to the model trainer 8016. The encoder 8008 may format the        quantizer output, such as by packing bits into words or data        into packets to make transmission more efficient.    -   11. The model trainer 8016 receives the update signal, decodes        it using a decoder 8010, and uses the model update to create an        updated master model 8018. For example, as shown, the model        trainer 8016 may add the update signal to parameters of the        master model 8018 to create new parameters for the updated        master model 8018.

The illustration and above description pertain to sending updates fromthe model updater 8002 to the model trainer 8016. The method described,including compression, quantization, sending select portions of a modelor signal, and use of a residual signal, may also be used for sendingthe pre-adapted model from the model trainer 8016 to the model updaters8002. Models may include acoustic models, language models, neuralnetworks, end-to-end ASR models, capitalization models, punctuationmodels, or other models. Model training may occur on-the-fly or fromstored data. Modifications, additions, or omissions may be made to theenvironment 8000 without departing from the scope of the presentdisclosure.

FIG. 75 illustrates an example environment 8100 for subdividing modeltraining, in accordance with some embodiments of the present disclosure.For efficiency or other reasons in training and adaptation of models,models may be divided into parts. In some embodiments, subdividing amodel may reduce the bandwidth when transmitting models between a modeltrainer and a model updater as described in this disclosure.Alternatively or additionally, subdividing may also reduce the amount ofdata and computation needed to adapt a model to a given speaker, groupof speakers, population, accent group, or training data sector.

In some embodiments, a model may be divided into multiple segments(model1 8102, model2 8104, model3 8106) and placed in series.Connections shown here as lines may represent signal paths between nodesand may include weights, so that each signal may be multiplied by aweight as it traverses the signal path. Select model segments may beupdated or may be updated frequently while other segments may remainstatic or may be updated less frequently. For example, a first portionof a DNN acoustic model, shown as model1 8102, may be particularlysensitive to speaker differences and therefore may be trained based on aspecific speaker's voice or on a group of speakers such as a group ofspeakers using the same communication device, while model2 8104 andmodel3 8106 may remain unchanged or may be updated less frequently.

For an acoustic model, the input to model1 8102 may be speech featuresand the output may be probabilities such as phoneme or other subwordprobabilities. For a language model, the input may be words, encodedwords, or words embeddings and the output may be conditional wordprobabilities. For an end-to-end ASR system, the input may be speechsamples or speech features and the output may be one or more words,characters, or subwords.

The illustration of models model1 8102, model2 8104, and model3 8106 asfeed-forward neural nets, the number of nodes and connections shown, andthe examples of inputs and outputs described above are provided asexamples. Other forms of models and other inputs and outputs may beused. Model segments are illustrated here in series; however, othertopologies are contemplated, including model segments in parallel asillustrated in FIG. 76, a combination of series and parallel, recurrentconnections, other neural net types, and with models inside orin-between other models.

The model segments are illustrated as including neural networks,however, the model segments may include other model forms includingGaussian mixture models, recurrent neural networks, linear estimators orclassifiers, and classifiers or estimators using kernel methods such assupport vector machines. Additional examples are listed in Table 9.

An example implementation for training models may include the followingoperations:

-   -   1. A transcription service collects voice samples from one or        more speakers.    -   2. A model trainer transmits a subset of a model (e.g., model2        8104) to a model updater.    -   3. The model updater tunes the model subset based on the voice        samples.    -   4. The model updater transmits a model update to the model        trainer.    -   5. The model trainer uses the model update to adapt the model        subset (e.g., model2 8104).    -   6. An ASR system uses the adapted model subset (e.g., model2        8104) and one or more other model subsets (e.g., model1 8102 and        model3 8106) to transcribe an audio signal and create a        transcription.    -   7. The transcription is sent to a user device to provide        transcriptions.

Furthermore, modifications, additions, or omissions may be made to theoperations described above without departing from the scope of thepresent disclosure. For example, the operations may be implemented indiffering order. Additionally or alternatively, two or more operationsmay be performed at the same time. Furthermore, the outlined operationsare only provided as examples, and some of the operations may beoptional, combined into fewer operations, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

FIG. 76 illustrates an example environment 8200 for subdividing modeltraining, in accordance with some embodiments of the present disclosure.In the depicted embodiment, the subdivided models are configured forupdates in a parallel model in contrast to the serial model asillustrated in FIG. 75. Multiple language or acoustic models,illustrated here as an example with model1 8102, model2 8104, and model38106 of FIG. 75, may be run in parallel, each presented withsubstantially the same input data and with the outputs averaged orsummed together to form an output. In the case of language models, thisarrangement may be a form of interpolation. In the case of acousticmodels, the arrangement may be used, for example, to determine subwordprobabilities averaged or summed across multiple acoustic models.

FIG. 77 illustrates an example environment 8300 for subdividing a model,in accordance with some embodiments of the present disclosure. In thedepicted embodiment, the subdivided models may be capable of beingreconfigured for use with one of multiple conditions such as a givenspeaker, group of speakers, acoustic environment, or topic. Asillustrated, a model may include, at a given time and depending onswitch settings, model A 8302, one of multiple switched models 8304 a-c,collectively the switched models 8304, and model B 8306.

In some embodiments, the switched models 8304 may each be trained on adifferent subset of training data or using different training methods.For example, each switched model 8304 may be trained on data from aspeaker or group of speakers. A data set may be divided into multipletraining sets based on speaker age, phone number, gender, accent,language, voice patterns, bandwidth, noise level, compression method andbit rate, topic, historical accuracy, etc., and using one or moremethods such as those shown in Table 4. Each training set may be used totrain one or more switched models 8304 while model A 8302 and model B8306 may be trained on multiple training sets. An example of a trainingset may be a speaker cluster, which may be a group of speakers thatsound alike or have similar voice characteristics. After creation, eachswitched model 8304 may be further adapted by training to the speaker orspeakers associated with that switched model, such as training to voicesamples or transcriptions from an ongoing communication session or fromrecorded data from previous communication sessions.

In some embodiments, a switch A 8308 and a switch B 8308 may connect oneof the switched models 8304 between model A 8302 and model B 8306. Theselection may be, for example, based on one or more of:

-   -   1. The switched model may be chosen based on speaker        characteristics such as age, gender, accent, language, or voice        patterns.    -   2. The switched model may be chosen based on signal        characteristics such as bandwidth, noise level, or compression        method and bit rate.    -   3. The switched model may be chosen based on the phone number or        other device identifier of the captioned caller.    -   4. The switched model may be chosen based on topic of        conversation.    -   5. The switched model may be chosen based on one or more        features in Table 2 and Table 5.    -   6. The switched model may be chosen to reduce an ASR system        error rate.    -   7. The switched model may be chosen based on a history of a        speaker during previous communication sessions. For example, if        a particular switched model was found on a previous        communication session to provide higher-accuracy for a given        speaker, then the same switched model may be used for the same        speaker in a subsequent communication session.    -   8. A given speaker's voice may be analyzed to determine which        speaker cluster he/she belongs to. The switched model associated        with that model cluster may be used for the given speaker.    -   9. The switched model that delivers the lowest perplexity        against a speaker's transcription may be chosen for use with a        given speaker. For example the switched model may be part of a        language model that may be used to measure perplexity of a        transcription of the speaker's voice sample.    -   10. The switched model that delivers the highest likelihood        score, which may be reported by an ASR system transcribing an        audio sample from the speaker, may be chosen.

As with FIG. 75, illustration of the models as feed-forward neural netsin a series of three is merely an example. Other topologies and modeltypes are possible. For example,

-   -   1. Models may be arranged in series, parallel, or combination        thereof.    -   2. The number of models connected at a given time may be more or        less than three.    -   3. There may be more or less than three switched models in a set        and more than one set of switched models.    -   4. Models may include other model types and other forms of        neural nets.    -   5. Switched models may be in other positions, such as at the        beginning or at the end of a series. For example, the        combination of models may include one of the switched models        8304 followed by model B 8306 or the combination of models may        include model A 8302 followed by one of the switched models        8304.    -   6. A feature transformation model may be at the start of a model        or series of models. For example, the feature transformation        model may receive a set of features from a feature extraction        step and transmit a set of features to the input of another        model. The feature transformation model may be a neural net, a        matrix that operates on the feature vector to create a        transformed feature vector, a vocal tract normalization (VTLN)        model, or another form of model. In one implementation, a        switched model adapted to the voice of one or more speakers may        be the first in a series of neural net acoustic model stages.        Since it is the first stage in an acoustic model, the switched        model may compensate for differences between speakers.

FIG. 78 illustrates an example environment 8400 for training modelson-the-fly, in accordance with some embodiments of the presentdisclosure. In some embodiments, training models on-the-fly may indicatethat the data used to train the models may be obtained in real-time asthe data is created from an on-going event and the data may be deletedduring and/or shortly after an end of the event. For example, the eventmay be a communication session. In these and other embodiments,communication session data of the communication session may be used totrain models during and/or shortly after an end of the communicationsession. In these and other embodiments, the communication session datamay be deleted or rendered unavailable for training during and/orshortly after an end of the communication session. In some embodiments,shortly after an end of an event may include an amount of time thatincludes 1, 2, 3, 5, 7, 10, 15, 20, 30, 45, 60, 120 or more seconds.Alternatively or additionally, shortly after an end of an event mayinclude an amount of time that may range between 1 second and 10minutes. Alternatively or additionally, the data used to train modelsusing the environment 8400 may be stored data.

In some embodiments, the environment 8400 may be configured to obtaindata that may be used to train models, such as ASR models. The data mayinclude audio and text of the audio. The audio may be regular audio orrevoiced audio. Alternatively or additionally, the data may include aseries of speech samples extracted from the audio. The speech samplesmay be extracted during frames of the audio. The frames may by 2, 5, 7,10, 15, 20, 30, 40, or 50 milliseconds, among other lengths. The textmay include a series of words representing the transcription of theaudio. Alternatively or additionally, the data may further include a setof endpoints that may include time markings indicating points in time inthe audio, such as where each word or subword from the text begins andends in the audio.

In some embodiments, the audio may be provided to a feature extractor8430. The feature extractor 8430 may be configured to extract featuresfrom the audio. In some embodiments, the feature extractor 8430 mayextract features from the audio for each frame of the audio. Thus, thefeatures may correspond to frames of the audio. The features may includesamples, spectral coefficients, MFCCs, or cepstral coefficients, amongother features. The feature extractor 8430 may provide the features to afeature transformer 8432. The feature transformer 8432 may be configuredto convert the features to a transformed feature set. In someembodiments, the feature transformer 8432 may use a DNN, MLLR matrix, orfMLLR matrix.

In some embodiments, the feature transformer 8432 may provide thetransformed feature set to a model processor 8402. The model processor8402 may be configured to apply a model that is to be trained to thetransformed feature set. In these and other embodiments, the model beingtrained may include a set of weights or parameters and the modelprocessor 8402 may be viewed as the processor that implements the modeland that adjusts the weights or parameters of the model to train oradjust the model. For example, the model processor 8402 may beconfigured to use the model being trained to map input features x1, x2,x3, . . . , xQ from the transformed feature set to output parameters a1,a2, . . . , aL. For example, the model processor 8402 may obtain one ormore features from the transformed feature set and output a set ofprobabilities. In these and other embodiments, each of the probabilitiesof the set of probabilities may correspond to a probability that aninput feature represents a particular phoneme given the context of theparticular phoneme. The model processor 8402 may provide the outputparameters to an error estimator 8408.

In the embodiment depicted in FIG. 78 and others, the model processor8402 may be used to implement an acoustic model. In these and otherembodiments, the model processor 8402 may serve as a probabilitycalculator, such as the probability calculator 508 from FIG. 5. Themodel processor 8402 may receive a set of features derived from an audiosignal as input and produce a set of probabilities, such as phonemeprobabilities, as output. When not training the model, the modelprocessor 8402 may output the set of probabilities to a decoder, such asthe decoder 510 (also from FIG. 5). When training the model, the modelprocessor 8402 may output the set of probabilities to the errorestimator 8408. Alternatively or additionally, the model processor 8402may implement a language model. In these and other embodiments, themodel processor 8402 may use the language model to provide probabilitiescorresponding to various word combinations to a decoder. Alternativelyor additionally, the model processor 8402 may implement any type ofmodel described in this disclosure.

In some embodiments, the environment 8400 may include agrapheme-to-phoneme converter 8404. The grapheme-to-phoneme converter8404 may be configured to receive the text of the audio. The text mayinclude words. The grapheme-to-phoneme converter 8404 may be configuredto determine a string of phonemes that correspond to the words. In someembodiments, the grapheme-to-phoneme converter 8404 may include alexicon, a pronunciation dictionary, and a set of letter-to-sound rulesthat specify how each word is pronounced. In these and otherembodiments, the grapheme-to-phoneme converter 8404 may analyze thewords using the lexicon, pronunciation dictionary, and letter-to-soundrules to determine the string of phonemes that corresponds to the words.For example, if an input word is “Bobby,” the grapheme-to-phonemeconverter 8404 may output the phoneme sequence “/b/ /aa/ /b/ /iy/”corresponding to the English phonemes that compose the word “Bobby.” Thegrapheme-to-phoneme converter 8404 may provide the phoneme sequence toan aligner 8422.

In some embodiments, the environment 8400 may further include an ASRsystem 8420. The ASR system 8420 may be configured to receive the audioand the text from the data obtained by the environment 8400. In theseand other embodiments, the ASR system 8420 may also obtain additionaldata, such as word endpoints. In some embodiments, the ASR system 8420may use the text as a grammar to be recognized and the audio as theinput to generate alignment marks. The alignment marks may indicate astart and an end of acoustic units in the audio. The acoustic unit maycorrespond to words, phonemes, and/or sub-phonemes. For example, thealignment marks may include start and end times for words, phonemes,and/or sub-phonemes in the audio. In some embodiments, sub-phonemes maybe components of a phoneme. For example, the sub-phonemes of the phoneme“t,” may include two sub-phonemes: (a) a closure (the silence beforeairflow begins) and (b) a plosive (sound created from turbulent airflowthat begins once the tongue drops from the hard palate). The ASR systemmay send the start and the end times of the acoustic units in the audioto the aligner 8422.

The aligner 8422 may be configured to obtain the phoneme sequence andthe start and the end of acoustic units in the audio. The aligner 8422may insert the start and the end of acoustic units in the audio into thephoneme sequence and provide the phoneme sequence with the insertedstart and end times to a vectorizer 8406.

In some embodiments, for each frame of the audio as used by the featureextractor 8430, the vectorizer 8406 may generate an idealizedrepresentation of the phoneme probabilities of the phoneme in the frameof the audio. In these and other embodiments, the phoneme probabilitiesmay be a probability of “1” for the phoneme present during a given frameof the audio and “0” for all other phonemes.

In some embodiments, the phoneme probabilities may be a probability ofthe phoneme independent of context. Alternatively or additionally, thephoneme probabilities may be a probability of the phoneme dependent oncontext referred to context-dependent phonemes. In these and otherembodiments, a phoneme dependent on context may be a phoneme in aspecified context. For example, a phoneme “/aa/” preceded by a phoneme“b” and followed by a phoneme “b” (i.e., “/b/ /aa/ /b/”) may be adifferent context than if the phoneme “/aa/” is preceded by a phoneme“b” and followed by a phoneme “d” (i.e., “/b/ /aa/ /d/).

In some embodiments, the vectorizer 8406 may provide an indication ofthe idealized probability of the phoneme to an error estimator 8408. Insome embodiments, the vectorizer 8406 may have one output for eachphoneme as illustrated. In these and other embodiments, the indicationof the idealized probability of the phoneme provided by the vectorizer8406 may include placing the output corresponding to the phoneme to “1”and having the rest of the outputs at “0.” The vectorizer 8406 maychange the output based on the frame of the audio being analyzed. Insome embodiments, when the phoneme probabilities relate to phonemesdependent on context, the number of potential outputs may be large. Ifthere are 41 phonemes, for example, there may be 41*41*41=68921 possibledifferent context-dependent phonemes. To reduce the number of outputs,context-dependent phonemes may be grouped into similar clusters, andcontexts that rarely or never occur may be eliminated. As a result, asmaller number of context-dependent phonemes may be provided to theerror estimator 8408.

The error estimator 8408 may obtain the output parameters from the modelprocessor 8402 and the indication of the probability of phonemes in theaudio from the vectorizer 8406. The error estimator 8408 may determinethe value of a cost function between the vectorizer 8406 output and themodel processor 8402 output. In some embodiments, the cost function maybe a total squared error. For example, if there are L outputs from thevectorizer 8406, the outputs of the vectorizer 8406 may be expressed asyj, j=1, 2, . . . L, and outputs of the model processor as aj, j=1, 2, .. . L, then the squared error may be determined:

$E^{2} = {\sum\limits_{j = 1}^{L}\; {\left( {y_{j} - a_{j}} \right)^{2}.}}$

Other cost functions such as cross entropy CTC (Connectionist TemporalClassification) loss function, sequential training, andsequence-discriminative training may also be used. The error estimator8408 may send an error signal to a tuner 8410. The error signal mayinclude a series of values of the cost function for each frame. In theseand other embodiments, the error signal may be used as a measure of howwell the model (e.g. weights or parameters w_(j)) fits the trainingdata. Training a model may include finding a set of model weights thatminimizes the error signal.

In some embodiments, the tuner 8410 may be configured to adjust modelparameters of a model being implemented by the model processor 8402 toreduce the cost function. Adjusting the model parameters may includetraining the model. The adjustment may happen iteratively based onmethods such as gradient descent where an update equation is defined.For example, if w_(j) is a parameter to be adjusted and μ is a learningrate, then, with each new data sample or batch of data samples, themodel parameter may be modified based on the update equation,

$\left. w_{j}\leftarrow{w_{j} - {\mu \frac{\delta \; E^{2}}{\delta \; w_{j}}}} \right.$

For faster computation, methods such as the chain rule andbackpropagation may also be used. The model trained by the modelprocessor 8402 may be used by other ASR systems, integrated into othermodels, and/or used by the model processor 8402 during generation oftranscriptions.

On-the-fly training methods disclosed herein for acoustic models (e.g.FIG. 78) and language models (e.g. FIG. 65) may be adapted for trainingother types of models such as capitalization and punctuation models.Other types of models include, but are not limited to, acoustic models,language models, confidence models, capitalization models, punctuationmodels, pronunciation models or lexicons, feature extraction ortransformation models, runtime parameters or settings, or other types ofmodels. Modifications, additions, or omissions may be made to theenvironment 8400 without departing from the scope of the presentdisclosure.

FIG. 79 is a flowchart of an example method 8500 of on-the-fly modeltraining, in accordance with some embodiments of the present disclosure.The method 8500 may be arranged in accordance with at least oneembodiment described in the present disclosure. The method 8500 may beperformed, in some embodiments, by processing logic that may includehardware (circuitry, dedicated logic, etc.), software (such as is run ona general-purpose computer system or a dedicated machine), or acombination of both. In some embodiments, the method may be performed bythe model trainer 7206 and/or the model updaters 7602 of FIGS. 68 and71, respectively. In these and other embodiments, the method 8500 may beperformed based on the execution of instructions stored on one or morenon-transitory computer-readable media. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, or eliminated, depending on the desiredimplementation.

The method 8500 may begin at block 8502, where first audio data of afirst communication session between a first device of a first user and asecond device of a second user may be obtained. In some embodiments, thefirst communication session may be configured for verbal communication.

At block 8504, during the first communication session, a first textstring that is a transcription of the first audio data may be obtained.In some embodiments, the first text string may be generated usingautomatic speech recognition technology. In these and other embodiments,the automatic speech recognition technology may generate the first textstring using revoicing of the first audio data.

In some embodiments, the first text string may be generated from one ormore words of a second text string and one or more words of a third textstring. In these and other embodiments, the second text string and thethird text string may be generated by automatic speech recognitiontechnology.

At block 8506, during the first communication session, a model of anautomatic speech recognition engine may be trained using the first textstring and the first audio data. In some embodiments, the model may bean acoustic model, a language model, a confidence model, orclassification model of the automatic speech recognition engine.Alternatively or additionally, the training of the model of theautomatic speech recognition engine using the first text string and thefirst audio data may complete after the first communication sessionends.

At block 8508, in response to completion of the training of the modelusing the first text string and the first audio data, the first audiodata and the first text string may be deleted. In some embodiments, thefirst audio data and the first text string may be deleted during thefirst communication session. Alternatively or additionally, the firstaudio data and the first text string may be deleted after the firstcommunication session.

At block 8510, after training the model using the first text string andthe first audio data, second audio data of a second communicationsession between a third device of a third user and a fourth device of afourth user may be obtained.

At block 8512, during the second communication session, a transcriptionof the second audio data may be generated by applying the model trainedusing the first text string and the first audio data. At block 8514, thetranscription of the second audio data may be provided to the fourthdevice for presentation during the second communication session.

Furthermore, modifications, additions, or omissions may be made to theoperations described above without departing from the scope of thepresent disclosure. For example, the operations may be implemented indiffering order. Additionally or alternatively, two or more operationsmay be performed at the same time. Furthermore, the outlined operationsare only provided as examples, and some of the operations may beoptional, combined into fewer operations, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments.

For example, the method 8500 may further include providing thetranscription of the first audio data to the second device forpresentation by the second device during the first communicationsession. As another example, the method 8500 may further includetraining, during the second communication session using a second textstring of the transcription of the second audio data, a second modelused by automatic speech recognition technology and in response tocompletion of the training of the second model using the second textstring and the second audio data, deleting the second audio data and thesecond text string.

FIG. 80 illustrates an example system 8600 for speech recognition, inaccordance with some embodiments of the present disclosure. In someembodiments, the system 8600 may include an ASR system 8620 that isconfigured with tunable acoustic models 8602 and tunable language models8604. The ASR system 8620 may be analogous to the ASR system 520 of FIG.5 and the tunable acoustic models 8602 and tunable language models 8604may be analogous to one or more of the models 530 of FIG. 5.

In some embodiments, the tunable acoustic models 8602 and tunablelanguage models 8604 may be tuned to improve quality of transcriptionsgenerated by the system 8600. Improving the quality may include reducingerrors and/or improving other characteristics of the transcriptions. Thetunable acoustic models 8602 and tunable language models 8604 may betuned, e.g., adjusted, for a given communication session, speaker, orgroup of speakers. For example, the system 8600 may evaluate a firstportion of a communication session and adapt one or more models ortuning parameters to improve accuracy during a second portion of thecommunication session. The tunable acoustic models 8602, the tunablelanguage models 8604, and other parameters may be tuned using one ormore of several methods, including:

-   -   1. The model weights or parameters may be adapted.    -   2. A parameter may be adjusted that affects the input or the        behavior of a model. For example, vocal tract length        normalization (VTLN) may be adjusted by determining a value for        a linear frequency warping factor. In another example, search        parameters such as beam width and a factor determining the        relative weight or contribution of an acoustic model compared to        a language model, may be adjusted.    -   3. A feature transformation model may be adjusted or selected.        The feature transformation model may be, for example, a        bottleneck DNN or a matrix of weights determined using MLLR or        fMLLR.    -   4. Multiple models may be combined to form a model. For example,        multiple models may be driven with the same input and the        outputs may be added or averaged (see FIGS. 81 and/or 82).        Hierarchal language models may be constructed by combining        language models with different characteristics or designed for        different tasks. For example, a generic language model, designed        to recognize a wide variety of input, may be combined with a        name language model, designed to recognize spoken names, in a        hierarchal structure. As another example, language models may be        interpolated to increase accuracy against a training set, which        may include a portion of a communication session or one or more        communication sessions with a speaker or group of speakers.    -   5. Multiple models may be arranged so that different models are        activated, depending on the nature of the audio sample and other        factors.    -   6. Multiple models may be run simultaneously. The model with the        highest performance may be chosen. In some embodiments, after        selection, other models may be disengaged. An example of this        method is in language detection, where audio may be transcribed        using multiple models, each trained for a different language or        dialect, until the spoken language is determined.    -   7. Models may be selected or tuned based on one or more factors,        including:        -   a. Analysis of the speaker's voice.        -   b. The device type (e.g., cell/speaker/landline phone).        -   c. Speaker demographics such as age (e.g., child, elderly),            gender, ethnicity, accent, location, speech or hearing            impairment, etc. Demographics may, for example, be            determined from customer records or other records, signal            analysis of the speaker's voice, and image analysis of a            speaker's picture.        -   d. Signal characteristics (e.g., loudness, SNR, signal            quality, signal bandwidth).        -   e. Conversation topic.        -   f. Information from past conversations.        -   g. Account type (business, residential, etc.).        -   h. Other features from Table 2 and Table 5.

FIG. 81 illustrates an example environment 8700 for selecting betweenmodels, in accordance with some embodiments of the present disclosure.In some embodiments, the environment 8700 may include a system 8730 thatmay be configured to generate transcriptions of audio. The system 8730may be an ASR system 8720 that may be configured to select and usedifferent acoustic and language models from acoustic models 8740 andlanguage models 8710, respectively. In some embodiments, the ASR system8720 may be analogous to the ASR system 520 of FIG. 5 and the acousticmodels 8740 and the language models 8710 may be analogous to one or moreof the models 530 of FIG. 5.

In some embodiments, the system 8730 may select an acoustic model fromamong the acoustic models 8740 depending on characteristics of the eventthat is generating the audio, such as a communication session.Alternatively or additionally, the system 8730 may select from among theacoustic models 8740 based on knowledge of the speaker represented inthe audio, historical information regarding one or more speakers in thecommunication session, characteristics of the communication device thatcaptures the voices of the speakers, or other factors (see FIG. 80).

In some embodiments, the acoustic models 8740 may include a static(i.e., parameters are constant) acoustic model. In these and otherembodiments, the static acoustic model may be used in combination withother selected acoustic models or alone. Alternatively or additionally,the acoustic models 8740 may include a dynamic acoustic model (see FIG.77, for example), designed to adapt to the voice of a particular speakeror group of speakers. In these and other embodiments, the dynamicacoustic model may be used in combination with other selected acousticmodels or alone.

In some embodiments, the system 8730 may be configured to interpolate,at an adder 8712, multiple language models 8710 to create aninterpolated language model. In some embodiments, a set of interpolationweights may determine the degree to which each language model isweighted in constructing the interpolated model. For example, twomodels, one trained on data collected from a transcription service andone trained on data collected elsewhere, may be interpolated together toform an interpolated model.

Alternatively or additionally, the system 8730 may not include the adder8712. In these and other embodiments, multiple language models 8710 maybe transmitted to the ASR system 8720 and the ASR system 8720 mayeffectively perform language model interpolation by combining theoutputs of the multiple language models. Additionally or alternatively,the ASR system 8720 may use interpolation weights in combining theoutputs of language models, such as by weighting a given conditionalword probability from each of multiple language models by the weightsfor their respective language models, and using the sum of the weightedprobabilities.

In some embodiments, additional models may be used for interpolationwith the language models. For example, a topic language model based onthe conversation topic of the audio for which transcriptions are beinggenerated may be used. The topic may be determined based on datacollected from the current event, such as the current communicationsession including content from a subscription party, a transcriptionparty, or both and from other participants on the current and/orprevious communication sessions, and/or from past events. In these andother embodiments, a topic classifier may identify a topic. The topicclassifier may identify a topic from a set of defined topics usingsimilarity measures such as tf-idf or cosine similarity or using amethod from Table 9. In these and other embodiments, the topic languagemodel may be built, selected, or adapted using the conversation topicidentified. Additionally or alternatively, a set of topic languagemodels, each covering one or more conversational topics, may each bebuilt using text pertaining to one or more topics. A topic detector mayselect one or more topic models for a current communication session,based on features such as analysis of the conversation, of pastconversations, characteristics of the participants, and account type,among other features. The selected topic models may be usedindividually, interpolated together, or interpolated with other languagemodels, by the ASR system 8720 to provide transcriptions for the currentcommunication session.

In some embodiments, the system 8730 may be configured to select fromamong the acoustic models 8740 and/or the language models 8710 for theASR system 8720 based on a language in the audio. For example, the audiomay include speech in English, Spanish, French, German, Chinese,Japanese, or other languages. In these and other embodiments, the system8730 may be configured to select a first acoustic model of the acousticmodels 8740 and a first language model of the language models 8710 basedon the language being a first language such as Spanish and may select asecond acoustic model of the acoustic models 8740 and a second languagemodel of the language models 8710 based on the language being a secondlanguage such as English. Additionally or alternatively, the system 8730may include multiple ASR systems. Each of the ASR systems may beconfigured for different sets of languages and a communication sessionin a selected language may be connected to the ASR system configured forthat language.

In some embodiments, the system 8730 may predict the language ordetermine the language of audio using one or multiple options. Theoptions to predict the language or determine the language of audio mayinclude one or more of:

-   -   1. A language detector 8714 may listen to audio, determine the        language being spoken, and direct the system 8730 to activate an        ASR system or model for that language.    -   2. The ASR system may start transcribing the audio using models        based on a first language determined, using one or more methods        such as those described in #6 below. The language detector 8714        may simultaneously listen to the audio and determine the        language being spoken. If the language being spoken is different        from the first language, then activate the ASR system or model        for the spoken language.    -   3. A non-revoiced ASR system may start transcribing the audio.        The language detector 8714 may determine the spoken language. An        estimator may estimate accuracy for the non-revoiced ASR system        transcribing the communication session. After at least some of        the audio has been transcribed, a first decision may be made,        based on estimated ASR accuracy and the language determined by        the language detector 8714, for (1) whether to continue        captioning with a non-revoiced ASR system, a revoiced ASR        system, or combination thereof (see Table 1) and a second        decision may be made for (2) which spoken language to use for        transcriptions (i.e., which spoken language should the        non-revoiced ASR system be configured for if the first decision        is a non-revoiced ASR system or which language skills should a        CA have if the first decision is to use a revoiced ASR system).        The spoken language detector may select a new spoken language to        use for transcriptions one or more times during the        communication session, at which time transcription may continue,        for example, using a different revoiced ASR system, a different        non-revoiced ASR system configured for the new language, or by        configuring the non-revoiced ASR system for the new language,        such as by the non-revoiced ASR system using one or more models        corresponding to the new language.    -   4. Mechanisms may be provided, for example via a user device or        a website, for a subscriber (party 1) to select a language for a        transcription party. The selection may be selectable        per-communication session or per-calling party and may be        remembered between communication sessions by, for example,        remembering the language selected for a given transcription        party and starting with the remembered language on a subsequent        communication session with the same transcription party. The        selection may be made prior to or during the communication        session and may be changed multiple times during the        communication session.    -   5. Mechanisms may be provided, for example via the transcription        party's calling device, sending DTMF signals, or visiting a web        site, for the transcription party to select a language.    -   6. The language detector 8714 may determine a likely spoken        language for a transcription party based on metadata such as:        -   a. A language indicator in the transcription party's or            subscriber's captioning account or profile, another account,            or customer record. The indicator may be settable, for            example, by an installer at installation of a captioning            service, by the caller, by the subscriber, by a customer            service representative, via an IVR system, or via a website.        -   b. A language indicator for the transcription party saved in            a caption calling subscriber's customer record or in a            profile on the captioned phone.        -   c. The primary language of the transcription party's country            or region, which may be determined based on information in            the transcription party's account or profile.        -   d. The transcription party's telephone number, device ID, or            IP address and the dominant language implied by the            corresponding location. For example, the transcription            party's language may be determined from the country code            and/or area code within the country.        -   e. The primary language of the subscriber's country or            region, which may be determined based on information in the            subscriber's account, profile, or customer record, telephone            number, IP address, or device identifier.        -   f. A language defined by the type of captioning service. For            example, if service is primarily provided for a particular            country, the spoken language may be determined to be the            dominant language for that country.        -   g. An analysis of the etymology of the transcription party's            name based on, for example, a language associated with            characters in the name, a name lookup table, or a language            classification of the name based on spelling. The            transcription party's name may be obtained, for example,            from a reverse telephone directory lookup, indexed by the            transcription party's phone number.        -   h. A language determined to have been spoken by the            transcription party on a previous communication session.        -   i. A language determined for one or more participants, other            than the transcription party, who are on the same            communication session.        -   j. A language determined for one or more contacts in the            subscriber's or transcription party's address book or            contact list.    -   7. One or more ASR systems may transcribe audio into multiple        languages, then select the language that yields the highest        objective score, such as estimated accuracy, given the audio        signal.    -   8. Transcriptions for two or more languages may be shown        simultaneously on the user device. The user may select the        preferred language.    -   9. The non-revoiced ASR system may be initially configured for        and generate transcriptions in a first language. If the ASR        confidence or another objective metric related to accuracy is        low, then the system may:        -   a. Transfer the communication session to a revoiced ASR            system.        -   b. Analyze the audio to determine the spoken language. If a            second language is detected, connect the communication            session to a non-revoiced ASR system configured for the            second language or to a revoiced ASR system associated with            a CA skilled in the second language.        -   c. Give the subscriber an option to change the selected            language.        -   d. Begin transcription in the next most likely language.    -   10. Start the communication session with a first revoiced ASR        system associated with a first CA. If the first CA determines        that he/she cannot revoice the language spoken, then the first        CA may indicate the language spoken or indicate that it is an        unknown language. Thereafter, a CA client may enable a CA to        select one of several options, including:        -   a. The first CA may transfer the communication session to a            non-revoiced ASR system configured for the transcription            party's language.        -   b. The first CA may transfer the communication session to a            second CA skilled in the transcription party's language.        -   c. The first CA may transfer the communication session to a            second CA skilled in the transcription party's language. The            communication session transfer may also connect a            non-revoiced ASR system configured for the transcription            party's language. If ASR confidence of the non-revoiced ASR            system exceeds a selected threshold, the second CA may be            dropped and the non-revoiced ASR system may take over.        -   d. The CA client may enable the first CA to transfer the            communication session to a system including one or more            non-revoiced ASR systems and language detectors.        -   e. The CA client may enable the first CA to transfer the            communication session to a system including one or more            non-revoiced ASR systems, revoiced ASR systems, and language            detectors.        -   f. The language detector 8714 may determine the spoken            language and connect the communication session to a revoiced            ASR system associated with a CA skilled in the spoken            language or to a non-revoiced ASR system configured for the            spoken language.    -   11. The ASR system may be configured to understand and        transcribe any of multiple languages. The ASR system, may, for        example, provide transcriptions for a subscriber in the language        determined to be most probable in light of a        language-independent acoustic and language models. For example,        the ASR system may use a language model that includes terms from        multiple languages and is trained on text in multiple languages.        The ASR system may use an acoustic model trained on data from        multiple languages. In some embodiments, models of acoustic        units such as phonemes in the acoustic model may be a set of        language-independent phonemes, at least some of which are        trained on audio from multiple languages. Additionally or        alternatively, the acoustic model may contain acoustic unit        models for multiple languages that, for example, run in parallel        and compete with each other.

When transcriptions of audio are not generated in real time because theinitial language assumption is incorrect or due to other delays, and asecond selected language may be changed during the communicationsession, recorded communication session audio from before the change inthe selected language may be processed by an ASR system configured totranscribe the spoken language so that the missing content, or a summarythereof, may be transcribed and displayed.

In some embodiments, when the environment 8700 is unable to identify thelanguage of the audio within a threshold of accuracy, multipletranscriptions may be generated. Each of the multiple transcriptions maybe generated in a separate language and each of the multipletranscriptions may be provided to a user device for presentation.

In some embodiments, transcriptions or the audio may be translated intoanother language using human translators or machine translation. Thesource language may be selected manually or using language detectionmethods such as those described above. The target language may beselected manually or based on the spoken language determined for therecipient (such as the subscriber). The translated transcriptions may beprovided independently or with the original transcriptions using adivided screen or multiple screens.

Modifications, additions, or omissions may be made to the environment8700 without departing from the scope of the present disclosure. Forexample, the acoustic models 8740 may include multiple static or dynamicacoustic models. Alternatively or additionally, multiple of the acousticmodels 8740 may be selected. In these and other embodiments, two or moreof the acoustic models 8740 models may be interpolated by an adder or bythe ASR system 8720. As another example, the adder 8712 may not beincluded. In these and other embodiments, a single one of the languagemodels 8710 may be selected or multiple of the language models 8710 maybe selected. In these and other embodiments, the language models 8710may include dynamic and/or static language models. The conceptsdiscussed above with respect to the acoustic and language models may beapplied to other types of models.

As another example, the system 8730 may be part of a user device. Inresponse to the system 8730 being unable to transcribe audio withaccuracy above a threshold, the user device may connect to an externaltranscription service that supports the spoken language. The user devicemay provide the audio to the external transcription service and obtainthe transcriptions from the external transcription service.Alternatively or additionally, the user device may download one or moremodels for the spoken language and transcribe the communication sessionusing the ASR system 8720.

FIG. 82 illustrates an example ASR system 8820 using multiple models, inaccordance with some embodiments of the present disclosure. The ASRsystem 8820 may include a feature extractor 8830, a feature transformer8832, a probability calculator 8840, and a decoder 8814.

In some embodiments, the feature extractor 8830 may be configured todetermine a first set of features from audio. The feature transformer8832 may convert the first set of features to a second set of features.The second set of features may be provided to the probability calculator8840. The probability calculator 8840 may determine a set of phonemeprobabilities. For example, the set of phoneme probabilities may includeconditional context-dependent phoneme probabilities. The probabilitycalculator 8840 may provide the phoneme probabilities to the decoder8814.

In some embodiments, the probability calculator 8840 may include first,second, and third acoustic models 8802 a, 8802 b, and 8802 c,collectively, the acoustic models 8802. The acoustic models 8802 may bearranged in series. The first and third acoustic models 8802 a and 8802c may be static. The second acoustic model 8802 b may be variable. Inthese and other embodiments, the second acoustic model 8802 b may beselected from first, second, and third optional acoustic models 8806 a,8806 b, and 8806 c, collectively the optional acoustic models 8806. Aselector 8804 may be configured to select one of the optional acousticmodels 8806 for providing to the probability calculator 8840 to use asthe second acoustic model 8802 b. The selector 8804 may be configured toselect the one of the optional acoustic models 8806 based on variousfeatures such as an identity of the speaker in the audio, analysis ofthe audio, historical information, or other factors such as those inTable 2 and Table 5. The probability calculator 8840 may use theacoustic models 8802 to determine the set of phoneme probabilities.

The decoder 8814 may use the set of phoneme probabilities andprobabilities from one or more language models, to generate atranscription. In some embodiments, the language models used by thedecoder 8814 may be arranged in a hierarchal structure. For example, thelanguage models may include a top language model 8808 that may containgeneric language information and word probabilities. The hierarchalstructure may also include multiple sub language models, includingfirst, second, and third sub-language models 8810 a, 8810 b, and 8810 c,collectively the sub-language models 8810. The sub-language models 8810may provide detail for particular topics and word or phrase classes.Alternatively or additionally, the sub-language models 8810 may includeprobabilities associated with specific users.

In some embodiments, one or more of the sub-language models 8810 may beinterpolated with the top language model 8808. In these and otherembodiments, the decoder 8814 may interpolate the sub-language models8810 and the top language model 8808 or another device may interpolatethe sub-language models 8810 and the top language model 8808. The one ormore of the sub-language models 8810 that may be interpolated with thetop language model 8808 may be selected based on various features suchas an identity of the speaker in the audio, analysis of the audio,historical information, or other factors such as those in Table 2 andTable 5. In these and other embodiments, the features may be the same,similar, or different than the features used to select among theoptional acoustic models 8806.

Modifications, additions, or omissions may be made to the ASR system8820 without departing from the scope of the present disclosure. Forexample, the acoustic models 8802 may be configured in a parallel orother configuration. Alternatively or additionally, language modelsstructure may not include a hierarchal structure. As another example,one or more of the first and third acoustic models 8802 a and 8802 c maybe omitted. As another example, in some embodiments, the ASR system 8820may include additional elements, such as a rescorer, grammar engine,and/or a scorer, among other elements.

FIG. 83 illustrates an example environment 8900 for adapting orcombining models, in accordance with some embodiments of the presentdisclosure. In some embodiments, the environment 8900 is configured toadapt or combine language models in response to communication sessiondata.

The environment 8900 may include a transcription unit 8914 that mayinclude an ASR system. The ASR system may include a language model thatmay be used to generate a transcription based on audio received by thetranscription unit 8914. The language model used by the ASR system maybe based on an interpolated language model that is the result ofmultiple language models that are interpolated together.

In some embodiments, the multiple language model may include a domainlanguage model 8901 and first, second, and third language models 8902 a,8902 b, and 8902 c, collectively the language models 8902. Theenvironment 8900 may further include an adder 8912. In some embodiments,the adder 8912 may combine the domain language model 8901 and thelanguage models 8902 to generate the interpolated language model. Insome embodiments, each of the domain language model 8901 and thelanguage models 8902 may be associated with an interpolated weight. Inthese and other embodiments, the adder 8912 may multiply a conditionalword probability for a given word from each language model by theinterpolation weight for the corresponding language model to create aweighted word probability. The weighted word probability for eachlanguage model may be summed to create a word probability for theinterpolated language model. In some embodiments, interpolation weightsmay be selected to reduce perplexity, increase a likelihood or loglikelihood score from an ASR system, or reduce error rate.

In some embodiments, the combining language models at the adder 8912 togenerate the interpolated language model may happen off-line such thatthe interpolated language model is created and stored in thetranscription unit 8914 before the transcription unit 8914 may begingenerating the transcription. Alternatively or additionally, thecombining language models at the adder 8912 to generate the interpolatedlanguage model may happen at runtime as the transcription unit 8914 isgenerating the transcription. In these and other embodiments, generatingthe interpolated language model at runtime may be performed by computinga weighted sum of each conditional word probability for use by the ASRsystem of the transcription unit 8914.

In some embodiments, a language model trainer 8920 may create or adaptthe domain language model 8901 using the communication session data fromthe current communication session or the current and past communicationsessions. Alternatively or additionally, at least one of the languagemodels 8902 may be a generic model that may be trained on data frommultiple services or data collections. In these and other embodiments,one or more of the language models 8902 may also be trained on datarelated to or derived from one or more of the following:

-   -   1. The topic of the current conversation.    -   2. Content from the transcription party.    -   3. A collection of transcription party data collected from        multiple communication sessions by a user device participating        in the current communication session.    -   4. The demographic of the transcription party (i.e., a language        model may be built from a collection of data from people who        match the demographic of the transcription party).    -   5. The account type of the transcription party.    -   6. One or more account types (see Table 10).    -   7. Data from the transcription party including data collected        from services other than the transcription service.    -   8. Data collected from participants in communication sessions in        one or more specified area codes or geographic regions.    -   9. Data collected from participants in communication sessions        with one or more specified accents or dialects.    -   10. Data collected from the current communication session.    -   11. Data collected from previous communication sessions with the        transcription party.    -   12. Data collected from text sources such as websites, books,        news feeds, transcriptions from radio, TV, and other broadcast        media, etc.    -   13. Data collected from text sources associated with one or more        calling parties such as email, journals, written documents,        blogs, posts on professional or social media sites, and contact        lists. Information extracted from such sources may include        vocabulary terms such as email addresses, street addresses,        names, and phone numbers.    -   14. Data collected from text sources related to the local area        of one or more participants in the communication session such as        local news services, websites for local businesses, or other        local information sources.    -   15. A set of names determined, using speech recognition, to have        been spoken on the communication session or on previous        communication sessions with the same subscriber.    -   16. A set of names determined, using speech recognition, to have        been spoken on the communication session or on previous        communication sessions with the same transcription party.    -   17. A language model trained on data from one of multiple spoken        languages (i.e., language-specific models).    -   18. Language models trained on data from multiple spoken        languages (i.e., language-independent models).    -   19. Language models trained on data from a cluster of        communication sessions, where cluster membership may be defined        by similarity between communication sessions. Clustering        membership may alternatively be determined using clustering        methods such as k-means or estimation-maximization (EM).    -   20. Text messages. These may be text messages sent or received        by calling parties, text sent between calling parties, or text        messages sent via services other than captioned services such as        SMS, MMS, and social media sites.    -   21. Data collected from a transcription service.    -   22. Data collected from non-transcription services such as call        center communication sessions, business communication sessions,        communication sessions to digital personal assistants, IVR        communication sessions, voicemail, etc.    -   23. Data collected from callers belonging to a specified group        or demographic such as speakers in a specified geographical        region, accented speakers, speakers with speech or hearing        impairments, children, elderly, male, female, business callers,        residential callers, etc.

In some embodiments, the language models 8901 and 8902 may result inimproved accuracy by incorporating vocabulary and statistics derivedfrom the data listed above, as well as from other data. An example ofhow the data may result in improved accuracy is now provided. A term maybe extracted from an utterance of a first participant in a communicationsession. A language model may be adjusted to give a higher weight to theextracted term. By giving the term a higher weight, there is anincreased probability that the language model may recognize the termwhen the term is spoken again by the first participant. Additionally oralternatively, a term extracted from an utterance or record from a firstparticipant may be used by a language model to increase the probabilityof detection for the term when spoken by a second participant who is onthe same communication session as the first participant.

In some embodiments, the environment 8900 may include an interpolationweight estimator 8904. The interpolation weight estimator 8904 may beconfigured to determine the interpolation weights for the languagemodels 8901 and 8902. In some embodiments, interpolation weightdetermination by the interpolation weight estimator 8904 may useon-the-fly interpolation where interpolation weights are assigned a setof initial values and adjusted based on data from each communicationsession. In these and other embodiments, the on-the-fly interpolationmay use a gradient descent algorithm to adjust the interpolationweights. In these and other embodiments, on-the-fly interpolation weightdetermination may avoid recording audio or text. As a result, on-the-flyinterpolation weight determination may be used when recording of audioor text is illegal or contractually prohibited.

Additionally or alternatively, the interpolation weight estimator 8904may use recorded and transcribed communication session information todetermine the interpolation weights. The recorded and transcribedcommunication session information may be referred to as a developmentset 8906. The development set 8906 may be reviewed or corrected by a setof transcription tools 8908 used by a transcriber. The interpolationweight estimator 8904 may be configured to use the development set 8906and information from the language models 8901, 8902 to determine theinterpolation weights. In some embodiments, the weights may be selectedto improve ASR accuracy and/or to reduce perplexity of the interpolatedlanguage models with respect to the development set 8906.

An example how the interpolation weights a1, a2, a3, etc., may bedetermined by the interpolation weight estimator 8904 follows:

-   -   1. Define a development set derived from one or more        transcriptions including N words, w(1), w(2), w(3), w(N), in        their original sequence.    -   2. Compute a conditional probability of each word in context        using each of the language models 8901 and 8902. If LM1 8901,        for example, is a trigram model, the conditional probability of        each word using LM1 may be expressed as:

$\quad\begin{matrix}{{P\left( {{{w(1)}{{context}(1)}},{{LM}\; 1}} \right)},{{{context}(1)} = ({none})}} \\{{P\left( {{{w(2)}{{context}(2)}},{{LM}\; 1}} \right)},{{{context}(2)} = {w(1)}}} \\{{P\left( {{{w(3)}{{context}(3)}},{{LM}\; 1}} \right)},{{{context}(3)} = {w(1)}},{w(2)}} \\{{P\left( {{{w(4)}{{context}(4)}},{{LM}\; 1}} \right)},{{{context}(4)} = {w(2)}},{w(3)}} \\\ldots \\{{P\left( {{{w(N)}{{context}(N)}},{{LM}\; 1}} \right)},{{{context}(N)} = {w\left( {N - 1} \right)}},{w\left( {N - 2} \right)}}\end{matrix}$

-   -   3. Define the conditional probability of a word w(i), i=1, N        using an interpolated model LM1 as a sum of the conditional        probabilities of the word using input language models LM1, LM2,        LMM multiplied by the weights a1, a2, . . . aM for the        corresponding input language model:

$\begin{matrix}{{P\left( {{{w(i)}{{context}(i)}},{LMI}} \right)} =} \\{{a\; 1*{P\left( {{{w(i)}{{context}(i)}},{{LM}\; 1}} \right)}} +} \\{{a\; 2*{P\left( {{{w(i)}{{context}(i)}},{{LM}\; 2}} \right)}} +} \\{\ldots +} \\{a\; M*{{P\left( {{{w(i)}{{context}(i)}},{LMM}} \right)}.}}\end{matrix}$

-   -   4. Define the log probability of the transcription, averaged        over each word, as:

$l = {\frac{1}{N}{\sum\limits_{i = 1}^{K}\; {\log_{2}\left\{ {P\left( {{{w(i)}{{context}(i)}},{LMI}} \right)} \right\}}}}$

-   -   5. Define perplexity as 2 to the power of the negative average        log probability of the transcription:

Perplexity=2^(−l)

-   -   6. Find weights a1, a2, . . . , aM that reduces the perplexity.

Additionally or alternatively, the development set 8906 may include aset of n-grams and counters for each n-gram. In these and otherembodiments, the n-grams may be derived from one or more communicationsessions. For example, n-grams may be derived from content spoken by atranscription party across one or more communication sessions.Alternatively or additionally, n-grams may be derived from multipleparties across multiple communication sessions. In these and otherembodiments, the interpolation weights may be determined by theinterpolation weight estimator 8904 as follows:

-   -   1. Define a development set including a set of K n-grams derived        from communication session data. In one example, for additional        privacy, communication session data may be used to count        n-grams, but not to create new n-grams. Each n-gram has an        associated counter c(1), c(2), c(3), . . . , C(K), indicating        how many times the n-gram appeared in the communication session        data. A table of n-grams may be expressed as follows:

Counter Word Context c(1) w(1) context(1) c(2) w(2) context(2) . . . . .. . . . c(K) w(K) context(K)

-   -   -   where the word is the last word of the n-gram and the            context is the previous words. For example, in the n-gram “I            like cats,” then “cats” is the word w and “I like” is the            context.

    -   2. Compute a conditional probability of each n-gram, using each        input language model. For example, for LM1:

$\quad\begin{matrix}{P\left( {{{w(1)}{{context}(1)}},{{LM}\; 1}} \right)} \\{P\left( {{{w(2)}{{context}(2)}},{{LM}\; 1}} \right)} \\{P\left( {{{w(3)}{{context}(3)}},{{LM}\; 1}} \right)} \\{P\left( {{{w(4)}{{context}(4)}},{{LM}\; 1}} \right)} \\\ldots \\{P\left( {{{w(K)}{{context}(K)}},{{LM}\; 1}} \right)}\end{matrix}$

-   -   3. Define the conditional probability of an n-gram g(i), i=1, K        using an interpolated model LM1 as a sum of the conditional        probabilities of the n-gram using the language models 8901 and        8902 multiplied by the weights a1, a2, a3, and a4 (for the        example shown with four language models and four weights, K=4)        for the corresponding input language model:

P(g(i) context(i), LM1)=a1*P(g(i)|context(i), LM1)+a2*P(g(i)|context(i),LM2)+a3*P(g(i)|context(i), LM3)+a4*P(g(i)|context(i), LM4).

-   -   4. Define the average log probability of the n-gram set as:

$l = {\frac{1}{\sum_{i = 1}^{K}{c(i)}}{\sum\limits_{i = 1}^{K}\; {{c(i)}*\log_{2}\left\{ {P\left( {{{w(i)}{{context}(i)}},{LMI}} \right)} \right\}}}}$

-   -   7. Define perplexity as 2 to the power of the negative average        log probability of the transcription:

Perplexity=2^(−l)

-   -   8. Find weights a1, a2, a3, a4 that reduces the perplexity.

In some embodiments, generating the interpolated model by the adder 8912using a development set 8906 of n-grams may include the followingoperations:

-   -   1. Train one or more language models by the prior language model        trainer 8910, denoted as prior LMs, LM2, LM3,        -   a. One of the prior models may be trained from a speaker or            group of speakers such as the transcription party, a group            of speakers using the transcription party device, a group of            speakers who have participated on communication sessions            with the transcription party, or multiple speakers on            multiple communication sessions.        -   b. One of the prior models may be built from n-grams            collected from one or more calling parties. The n-grams may            be collected from recorded communication session data or            collected from communication session data on-the-fly (as it            is transcribed for a transcription service in an arrangement            where data is not recorded, but rather is created, used to            count n-grams, and then deleted once it is no longer            needed). Collecting n-grams on-the-fly is described in            greater detail with reference to FIG. 61.        -   c. One of the prior models may be built on-the-fly. For            example, a prior model may be a neural net language model            trained on-the-fly from transcription data (see FIG. 70).    -   2. Generate and collect new data, such as from a transcription        service or other service.        -   a. Data may be stored or may only persist briefly. If data            is not stored, the language model training step #3 may be            performed on-the-fly.        -   b. Data may be collected from a single communication            session, for a single user over multiple phone communication            sessions, from a collection of users, across users in a            geographic region such as an accent region, from a            collection of communication sessions at a specific moment in            time, from a collection of communication sessions over a            period of time, etc.        -   c. If data is stored, a transcriber may transcribe some or            all of the audio into text. In some embodiments, available            CAs may be used as transcribers. If data is not stored (such            as when there is insufficient consent or when it is            otherwise not allowed or practical), transcription may be            automatic (e.g., via ASR) or performed by one or more human            labelers in real-time.    -   3. Create a domain language model 8901 from the new data        collected in #2 above.    -   4. Create a development set 8906 from transcription service        data.        -   a. The development set 8906 may be a transcription or            portions of transcriptions from one or more transcribed            communication sessions.        -   b. The development set 8906 may be collected from a first            portion of a current communication session.        -   c. The development set 8906 may be a set of n-grams.        -   d. As an alternative to using data from the transcription            service (which may be problematic due to privacy concerns),            the development set 8906 may be derived from a separate            database or service such as from paid or volunteer subjects            who provide consent to record their communication sessions            or from a data collection from a different service.    -   5. Using the development set 8906 and one or more prior language        models from the prior language model trainer 8910, determine a        set of interpolation weights a1, a2, etc. The weights may be        designed, for example, to decrease perplexity and/or increase        accuracy on the development set.        -   a. If data is stored, interpolation weights may be            calculated using the interpolation method in the first            implementation above.        -   b. If data is not stored, the interpolation weight estimator            8904 may search for the weights in real-time. Since only            part of the data is available at a time, the interpolation            weight estimator 8904 may use a gradient descent method that            iteratively adapts the weights in small steps as segments of            text data are available. The initial value of the            interpolation weights may be a set determined using a            different development set or they may be from a set used for            the transcription party on a previous communication session.        -   c. Equations from the steps described above may be used to            determine interpolation weights.    -   6. Combine the prior language models into one or more        interpolated language models. For example, if the language model        includes probabilities of, say, a given word given the context        of the preceding few words, the interpolated language model may        be a weighted average of the corresponding probability in each        language model for the same word and context. The weights in the        figure are denoted as a1, a2, a3, a4, where a1 may be the weight        of the domain-specific language model, and a2, a3, a4, may be        weights for the other corresponding prior language models.        -   a. The interpolated model may be created offline.        -   b. The interpolated model probabilities may be determined at            run-time, as needed by an ASR system using the weight values            and the prior language models.    -   7. Provide the interpolated language model (or, for runtime        interpolation, the prior language models LM1, LM2, and the        interpolation weights) to the ASR system. In one embodiment, the        interpolated language model may be used in a second portion of a        current communication session.

Additionally or alternatively, generating the interpolated model by theadder 8912 using a development set 8906 of n-grams may include thefollowing operations:

-   -   1. Collect n-grams from one or more callers, such as a        particular first transcription party, or group of callers,        across one or more communication sessions.        -   a. N-grams may be derived from recorded communication            session data. Recorded communication session data may be            captured using a privacy filter.        -   b. N-gram collection may be on-the-fly.        -   c. N-gram collection may include a privacy filter.        -   d. In some embodiments, n-gram collection may include            counting existing n-grams only, not creating new n-grams.    -   2. Create a development set based on or formed of the collected        n-grams.    -   3. Using n-grams collected from the one or more callers, create        a language model.    -   4. Using the development set and one or more prior language        models, determine a set of interpolation weights a1, a2, etc.        -   a. The weights may be selected to achieve a statistic            derived from the development set.        -   b. The weights may be selected to reduce perplexity on the            development set.        -   c. The weights may be selected to reduce ASR error rate on            the development set.        -   d. One or more of the language models may be based on            n-grams.        -   e. One or more of the language models may use neural            networks.        -   f. Equations from the second implementation (above) may be            used to determine interpolation weights.    -   5. Using the interpolation weights, create one or more        interpolated models.        -   a. The interpolated model may be created offline.        -   b. The interpolated model probabilities may be determined at            run-time, as needed by an ASR system using the weight values            and the prior language models.    -   6. Use the interpolated model with an ASR system to transcribe        speech for the one or more callers.        -   a. The interpolated model may be used during a communication            session from which the development set of n-grams were            collected.        -   b. N-grams may be collected from a first communication            session and used as a development set to train an            interpolated model. The interpolated model may then be used            to transcribe a second communication session occurring after            the first communication session. In some embodiments, the            second communication session may include the first            transcription party. Additionally or alternatively, the            second communication session may include a second            transcription party.

Furthermore, modifications, additions, or omissions may be made to theoperations described above without departing from the scope of thepresent disclosure. For example, the operations may be implemented indiffering order. Additionally or alternatively, two or more operationsmay be performed at the same time. Furthermore, the outlined operationsare only provided as examples, and some of the operations may beoptional, combined into fewer operations, or expanded into additionaloperations without detracting from the essence of the disclosedembodiments. For example, the operation of generate and collect newdata, such as from a transcription service or other service, may not beperformed.

The methods embodied herein for training or adapting models from storeddata or on-the-fly may use specific model types as examples but may beadapted to training various types of models, including acoustic models,language models, confidence models, capitalization models, punctuationmodels, pronunciation models or lexicons, feature extraction ortransformation models, runtime parameters or settings or other types ofmodels.

Modifications, additions, or omissions may be made to the environment8900 without departing from the scope of the present disclosure. Forexample, in some embodiments, the environment 8900 may not include theadder 8912. In these and other embodiments, the adder 8912 may bereplaced by runtime interpolation inside the ASR system (e.g., ASRsystem 8720) of the transcription unit 8914. Runtime interpolation maycompute probabilities of each n-gram as it is needed by the ASR system.In this way, rather than create an entire language model in advance, theindividual language model elements (e.g., conditional probabilities) maybe computed as needed by weighting and adding probabilities from inputmodels.

In the various arrangements described above with reference to FIGS. 1-83for generating transcriptions, functions such as ASR, fusion,estimation, selection, training, etc., may be illustrated as part of aparticular hardware device or system. It is to be understood that thesefunctions may in at various locations (and not necessarily the samelocation as each other) and that other hardware arrangements arepossible, including:

-   -   1. Transcription functions such as ASR, fusion, model training,        estimation, and selection may run at various locations,        including:        -   a. Hardware supporting the primary transcription unit            assigned to the communication session.        -   b. An available transcription unit attached to the            communication session to provide processing resources. For            example, the primary transcription unit (#1, above) may            handle a communication session and a second transcription            unit, otherwise unused at the moment, may be used to provide            additional ASR resources for the same communication session.        -   c. A CA workstation.        -   d. A user device. In some embodiments, the user device may            display ASR results on a display. Alternatively or in            addition, the user device may transmit ASR results to a            transcription unit, such as one associated with a CA. The            transcription unit may correct errors in the ASR result to            create a corrected transcription, which may be sent back to            the user device for display or to correct previously            displayed transcriptions.        -   e. A PC, tablet, smartphone, household appliance, or other            computer digitally connected to, in communication with, or            paired with the user device. The computer may be owned by            the subscriber or in the subscriber's home.        -   f. A transcription party's device.        -   g. A server running in a network such as the network at a            captioning center, a cluster of ASR machines, or a cloud            service.        -   h. A virtual machine running on a network server.    -   2. An ASR system listening to a CA's voice or to a caller's        voice may run on a CPU core, on multiple CPU cores, or on        multiple CPUs.    -   3. Transcription functions may be assigned to separate cores on        one or more CPUs. For example, an ASR system listening to a CA's        voice may run on one core, one or more ASR systems listening to        a caller may each run on one or more other cores, and training,        selection, and fusion may each run on one or more other cores.    -   4. One or more transcription functions may run on a cloud or        network service or on a server cluster. The server cluster may,        for example, be at a transcription service provider location, at        an ASR provider location, or may run on a cloud service.    -   5. One or more transcription functions may each run on one or        more separate CPUs, which may be local to a transcription unit        or remote and may be accessed via a network.    -   6. In embodiments where an ASR system is described, it is to be        understood that one or more ASR systems may be replaced by an        API interface which sends audio to one or more ASR systems and        receives a return transcription over the API interface.    -   7. In embodiments herein where an ASR system is described, it is        to be understood that the ASR system may include components such        as multiple ASR systems, one or more fusers, text editors,        rescorers, among other components.    -   8. At least part of one or more transcription functions may run        on a coprocessor.    -   9. At least part of one or more transcription functions may run        on a vector processor such as a SIMD device, such as a GPU. The        vector processor may be, for example, part of a CA workstation,        part of a speech recognition server, or part of a captioned        phone. Where the current disclosure refers to a CPU, it is to be        understood that a GPU, TPU (tensor processing unit), or other        processor may also be used.    -   10. One or more transcription functions may share a core, CPU,        or vector processor.    -   11. One or more transcription functions may each be allocated        exclusive use of a fixed memory space. Alternatively or        additionally, one or more transcription functions may share        memory space, where memory contents for a first function may be        swapped to an alternate location if the space is needed by a        second function. Memory may include solid state memory such as        RAM, hard disk, solid state drives, and optical drives.    -   12. A core, CPU, vector processor, or server may process speech        for multiple simultaneous audio inputs.

The processing elements discussed above, such as the CPUs, GPUs, TPUs,processing cores, and other hardware may include any number ofprocessors or processing elements configured to, individually orcollectively, perform or direct performance of any number of operationsdescribed in the present disclosure.

FIG. 84 illustrates an example computing system 9100 that may beconfigured to perform operations and methods disclosed herein. Thecomputing system 9100 may be configured to implement or direct one ormore operations associated with the embodiments described in thisdisclosure. For example, in some embodiments, the computing system 9100may be included in or form part of a transcription service or any of theabove listed devices and/or systems or other devices and/or systemsdescribed in this disclosure. The computing system 9100 may include aprocessor 9110, a memory 9112, and a data storage 9114. The processor9110, the memory 9112, and the data storage 9114 may be communicativelycoupled.

In general, the processor 9110 may include any suitable special-purposeor general-purpose computer, computing entity, or processing deviceincluding various computer hardware or software modules and may beconfigured to execute instructions stored on any applicablecomputer-readable storage media. For example, the processor 9110 mayinclude a microprocessor, a microcontroller, a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aField-Programmable Gate Array (FPGA), or any other digital or analogcircuitry configured to interpret and/or to execute program instructionsand/or to process data. Although illustrated as a single processor inFIG. 91, the processor 9110 may include any number of processorsconfigured to, individually or collectively, perform or directperformance of any number of operations described in the presentdisclosure, including incorporating any of the described hardware inthis disclosure. Additionally, one or more of the processors may bepresent on one or more different electronic devices, such as differentservers.

In some embodiments, the processor 9110 may be configured to interpretand/or execute program instructions and/or process data stored in thememory 9112, the data storage 9114, or the memory 9112 and the datastorage 9114. In some embodiments, the processor 9110 may fetch programinstructions from the data storage 9114 and load the programinstructions in the memory 9112. After the program instructions areloaded into memory 9112, the processor 9110 may execute the programinstructions.

The memory 9112 and the data storage 9114 may include computer-readablestorage media for carrying or having computer-executable instructions ordata structures stored thereon. Such computer-readable storage media mayinclude any available media that may be accessed by a general-purpose orspecial-purpose computer, such as the processor 9110. By way of example,and not limitation, such computer-readable storage media may includetangible or non-transitory computer-readable storage media includingRandom Access Memory (RAM), Read-Only Memory (ROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-OnlyMemory (CD-ROM) or other optical disk storage, magnetic disk storage orother magnetic storage devices, flash memory devices (e.g., solid statememory devices), or any other storage medium which may be used to carryor store particular program code in the form of computer-executableinstructions or data structures and which may be accessed by ageneral-purpose or special-purpose computer. In these and otherembodiments, the term “non-transitory” as explained in the presentdisclosure should be construed to exclude only those types of transitorymedia that were found to fall outside the scope of patentable subjectmatter in the Federal Circuit decision of In re Nuijten, 500 F.3d 1346(Fed. Cir. 2007). Combinations of the above may also be included withinthe scope of computer-readable media.

In some embodiments, the different components, modules, engines, andservices described herein may be implemented as objects or processesthat execute on a computing system (e.g., as separate threads). Whilesome of the systems and methods described herein are generally describedas being implemented in software (stored on and/or executed by generalpurpose hardware), specific hardware implementations or a combination ofsoftware and specific hardware implementations are also possible andcontemplated.

In accordance with common practice, the various features illustrated inthe drawings may not be drawn to scale. The illustrations presented inthe present disclosure are not meant to be actual views of anyparticular apparatus (e.g., device, system, etc.) or method, but aremerely idealized representations that are employed to describe variousembodiments of the disclosure. Accordingly, the dimensions of thevarious features may be arbitrarily expanded or reduced for clarity. Inaddition, some of the drawings may be simplified for clarity. Thus, thedrawings may not depict all of the components of a given apparatus(e.g., device) or all operations of a particular method.

Terms used herein and especially in the appended claims (e.g., bodies ofthe appended claims) are generally intended as “open” terms (e.g., theterm “including” should be interpreted as “including, but not limitedto,” the term “having” should be interpreted as “having at least,” theterm “includes” should be interpreted as “includes, but is not limitedto,” etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, it is understood that such recitation should beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,etc.” or “one or more of A, B, and C, etc.” is used, in general such aconstruction is intended to include A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B, and C together,etc. For example, the use of the term “and/or” is intended to beconstrued in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” should be understood to include the possibilities of “A”or “B” or “A and B.”

Additionally, the use of the terms “first,” “second,” “third,” etc., arenot necessarily used herein to connote a specific order or number ofelements. Generally, the terms “first,” “second,” “third,” etc., areused to distinguish between different elements as generic identifiers.Absence a showing that the terms “first,” “second,” “third,” etc.,connote a specific order, these terms should not be understood toconnote a specific order. Furthermore, absence a showing that the termsfirst,” “second,” “third,” etc., connote a specific number of elements,these terms should not be understood to connote a specific number ofelements. For example, a first widget may be described as having a firstside and a second widget may be described as having a second side. Theuse of the term “second side” with respect to the second widget may beto distinguish such side of the second widget from the “first side” ofthe first widget and not to connote that the second widget has twosides.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that the variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the present disclosure.

1. A method comprising: obtaining first audio data of a firstcommunication session between a first device of a first user and asecond device of a second user, the first communication sessionconfigured for verbal communication; obtaining, during the firstcommunication session, a first text string that is a transcription ofthe first audio data; training, during the first communication session,a model of an automatic speech recognition system using the first textstring and the first audio data; in response to completion of thetraining of the model using the first text string and the first audiodata, deleting the first audio data and the first text string; aftertraining the model using the first text string and the first audio data,obtaining second audio data of a second communication session between athird device of a third user and a fourth device of a fourth user;generating, during the second communication session, a transcription ofthe second audio data by applying the model trained using the first textstring and the first audio data; and providing the transcription of thesecond audio data to the fourth device for presentation during thesecond communication session.
 2. The method of claim 1, wherein themodel is an acoustic model, a language model, a confidence model, orclassification model of the automatic speech recognition system.
 3. Themethod of claim 1, wherein the first text string is generated usingautomatic speech recognition technology.
 4. The method of claim 3,wherein the automatic speech recognition technology generates the firsttext string using a revoicing of the first audio data.
 5. The method ofclaim 1, wherein the first text string is generated from one or morewords of a second text string and one or more words of a third textstring, the second text string and the third text string generated byautomatic speech recognition technology.
 6. The method of claim 1,wherein the training of the model of the automatic speech recognitionsystem using the first text string and the first audio data completesafter the first communication session.
 7. The method of claim 1, whereinthe first audio data and the first text string are deleted during thefirst communication session.
 8. The method of claim 1, furthercomprising providing the transcription of the first audio data to thesecond device for presentation by the second device during the firstcommunication session.
 9. The method of claim 1, further comprising:training, during the second communication session using a second textstring of the transcription of the second audio data and the secondaudio data, a second model used by automatic speech recognitiontechnology; and in response to completion of the training of the secondmodel using the second text string and the second audio data, deletingthe second audio data and the second text string.
 10. At least onenon-transitory computer-readable media configured to store one or moreinstructions that in response to being executed by at least onecomputing system cause performance of the method of claim
 1. 11. Amethod comprising: obtaining first audio data of a first communicationsession between a first device of a first user and a second device of asecond user, the first communication session configured for verbalcommunication; obtaining, during the first communication session, afirst text string that is a transcription of the first audio data;training, during the first communication session, a model of anautomatic speech recognition system using the first text string and thefirst audio data; in response to completion of the training of the modelusing the first text string and the first audio data, deleting the firstaudio data and the first text string; after deleting the first audiodata and the first text string, obtaining second audio data of a secondcommunication session between a third device of a third user and afourth device of a fourth user; obtaining, during the secondcommunication session, a second text string that is a transcription ofthe second audio data; and further training, during the secondcommunication session, the model of the automatic speech recognitionsystem using the second text string and the second audio data.
 12. Themethod of claim 11, wherein the model is an acoustic model, a languagemodel, a confidence model, or classification model of the automaticspeech recognition system.
 13. The method of claim 11, wherein the firsttext string is generated using automatic speech recognition technology.14. The method of claim 13, wherein the automatic speech recognitiontechnology generates the first text string using a revoicing of thefirst audio data.
 15. The method of claim 11, wherein the first textstring is generated from one or more words of a second text string andone or more words of a third text string, the second text string and thethird text string generated by automatic speech recognition technology.16. The method of claim 11, wherein the training of the model of theautomatic speech recognition system using the first text string and thefirst audio data completes after the first communication session. 17.The method of claim 11, wherein the first audio data and the first textstring are deleted during the first communication session.
 18. Themethod of claim 11, further comprising: providing the transcription ofthe first audio data to the second device for presentation by the seconddevice during the first communication session; and providing thetranscription of the second audio data to the fourth device forpresentation by the fourth device during the second communicationsession.
 19. The method of claim 11, wherein the first audio dataoriginates from the first device and is based on captured verbalcommunications of the first user during the first communication session.20. At least one non-transitory computer-readable media configured tostore one or more instructions that in response to being executed by atleast one computing system cause performance of the method of claim 11.